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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "UOzd0zRXW5Uv"
+   },
+   "source": [
+    "# Week-1 Assignment\n",
+    "\n",
+    "*Welcome to your first assignment for the SimuTech Winter Project 2022! I hope you are excited to implement and test everything you have learned up until now. There is an interesting set of questions for you to refine your acquired skills as you delve into hands-on coding and deepen your understanding of numpy, pandas, and data visualization libraries.*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "2XFUXyq9Y9TG"
+   },
+   "source": [
+    "# Section 0 : Importing Libraries"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "D_bzcLusjSO0"
+   },
+   "source": [
+    "*Let's begin by importing numpy, pandas and matplotlib.*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "id": "jdwX7bI7aGeY"
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib as mp"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "LM25mz5ta-Q_"
+   },
+   "source": [
+    "# Section 1 : Playing with Python and Numpy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "TcFQVqhAdQw_"
+   },
+   "source": [
+    "### Q1. Matrix Multiplication"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "6Dcai9pys0j8"
+   },
+   "source": [
+    "##### (i) Check if matrix multiplication is valid"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "id": "0qC4txIss3gG"
+   },
+   "outputs": [],
+   "source": [
+    "\n",
+    "def isValid(A,B):\n",
+    " c1=A.shape[1]\n",
+    " r2=B.shape[0]\n",
+    " if c1==r2:\n",
+    "  return True\n",
+    " else:\n",
+    "  return False\n",
+    "    \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "OPTfRGlTdXxa"
+   },
+   "source": [
+    "##### (ii) Using loops (without using numpy)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "id": "V8F5ETsRct3S"
+   },
+   "outputs": [],
+   "source": [
+    "def matrix_multiply(A,B):\n",
+    " r1,c1=A.shape\n",
+    " r2,c2=B.shape\n",
+    " z=np.zeros((r1,c2))\n",
+    " for a in range(len(A)):      \n",
+    "   for b in range(len(B[0])):      \n",
+    "          for c in range(len(B)):      \n",
+    "               z[a][b] += A[a][c] * B[c][b] \n",
+    " return(z)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "umUgUTSWdos8"
+   },
+   "source": [
+    "##### (iii) Using numpy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "id": "-tdXlCHrduBP"
+   },
+   "outputs": [],
+   "source": [
+    "def matrix_multiply_2(A,B):\n",
+    " return(np.dot(A,B))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "2nMFnl84edJG"
+   },
+   "source": [
+    "##### (iv) Testing your code\n",
+    "\n",
+    "Run the following cell to check if your functions are working properly.\n",
+    "\n",
+    "*Expected output:*\n",
+    "[ [102 108 114]\n",
+    " [246 261 276]\n",
+    " [390 414 438]\n",
+    " [534 567 600] ]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {
+    "id": "09rX_Cm9ezmq"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Result using loops: [[102. 108. 114.]\n",
+      " [246. 261. 276.]\n",
+      " [390. 414. 438.]\n",
+      " [534. 567. 600.]]\n",
+      "Result using numpy: [[102 108 114]\n",
+      " [246 261 276]\n",
+      " [390 414 438]\n",
+      " [534 567 600]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "A = np.array([\n",
+    "    [1, 2, 3],\n",
+    "    [4, 5, 6],\n",
+    "    [7, 8, 9],\n",
+    "    [10, 11, 12]\n",
+    "])\n",
+    "\n",
+    "B = np.array([\n",
+    "    [13, 14, 15],\n",
+    "    [16, 17, 18],\n",
+    "    [19, 20, 21]\n",
+    "])\n",
+    "\n",
+    "if isValid(A,B):\n",
+    "  print(f\"Result using loops: {matrix_multiply(A,B)}\")\n",
+    "  print(f\"Result using numpy: {matrix_multiply_2(A,B)}\")\n",
+    "else:\n",
+    "  print(f\"Matrix multiplication is not valid\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "5hfP7Ao1fwqV"
+   },
+   "source": [
+    "### Q2. Z-Score Normalisation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "0e81Xx5Kw_CQ"
+   },
+   "source": [
+    "Z-score normalization refers to the process of normalizing every value in a dataset such that the mean of all of the values is 0 and the standard deviation is 1.\n",
+    "\n",
+    "We use the following formula to perform a z-score normalization on every value in a dataset:\n",
+    "\n",
+    "New value = (x – μ) / σ\n",
+    "\n",
+    "where:\n",
+    "\n",
+    "x: Original value\n",
+    "\n",
+    "μ: Mean of data\n",
+    "\n",
+    "σ: Standard deviation of data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "GP_MuyUbguSR"
+   },
+   "source": [
+    "##### (i) Without using numpy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "id": "0vix8qaaghwP"
+   },
+   "outputs": [],
+   "source": [
+    "def mean(x):\n",
+    " i=0\n",
+    " b=0\n",
+    " for a in x:\n",
+    "    b=b+a\n",
+    "    i+=1\n",
+    " return(b/i)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "id": "j1qabel-g_f5"
+   },
+   "outputs": [],
+   "source": [
+    "import math\n",
+    "def standard_deviation(x):\n",
+    " i=0\n",
+    " s=0\n",
+    " m=mean(x)\n",
+    " for b in x:\n",
+    "  s+=(b-m)*(b-m)\n",
+    "  i+=1\n",
+    "    \n",
+    " u=math.sqrt(s/i)\n",
+    " return(u)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "id": "797ewbNqhEpY"
+   },
+   "outputs": [],
+   "source": [
+    "def zscore_normalisation(x):\n",
+    "  p=standard_deviation(x)\n",
+    "  q=mean(x)\n",
+    "  a=0\n",
+    "  i=0\n",
+    "  for a in x:\n",
+    "    x[i]=(a-q)/p\n",
+    "    i+=1\n",
+    "  return(x)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "k8dq1wqRhbUB"
+   },
+   "source": [
+    "##### (ii) Using numpy\n",
+    "\n",
+    "Numpy has in_built functions for calculating mean and standard deviation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "id": "WjxJTUDihsYg"
+   },
+   "outputs": [],
+   "source": [
+    "def zscore_normalisation_2(x):\n",
+    "  m=np.mean(x)\n",
+    "  s=np.std(x)\n",
+    "  b=(x-m)/s\n",
+    "  return(b)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "glRqKU-_1pue"
+   },
+   "source": [
+    "##### (iii) Testing your code"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Kl7XBymOigCU"
+   },
+   "source": [
+    "Run the following cell to check if your functions are working properly.\n",
+    "\n",
+    "*Expected Output:* [-1.06753267 -0.99745394 -0.99745394 -0.81057732 -0.41346451 -0.06307086\n",
+    "  0.31068237  0.91803138  1.22170588  1.89913361]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "id": "78ptZxf6ipZp"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Result without using numpy: [-1.0675326683028088, -0.9974539373420117, -0.9974539373420117, -0.8105773214465528, -0.41346451266870277, -0.06307085786471743, 0.3106823739262003, 0.9180313755864415, 1.2217058764165623, 1.8991336090376005]\n",
+      "Result using numpy: [-1.06753267 -0.99745394 -0.99745394 -0.81057732 -0.41346451 -0.06307086\n",
+      "  0.31068237  0.91803138  1.22170588  1.89913361]\n"
+     ]
+    }
+   ],
+   "source": [
+    "x = [4, 7, 7, 15, 32, 47, 63, 89, 102, 131]\n",
+    "print(f\"Result without using numpy: {zscore_normalisation(x)}\")\n",
+    "print(f\"Result using numpy: {zscore_normalisation_2(x)}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "0WVscJ0z14rp"
+   },
+   "source": [
+    "### Q3. Sigmoid fn and its derivative"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Jfntb0Rg94Tu"
+   },
+   "source": [
+    "The sigmoid function is a mathematical function that maps any input value to a value between 0 and 1.\n",
+    "\n",
+    "It is defined mathematically as s(x) = 1/(1+e^(-x))."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "8SCAPkjO2m7u"
+   },
+   "source": [
+    "##### (i) Write a fn to implement sigmoid fn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "id": "jL_TRQFG2l5m"
+   },
+   "outputs": [],
+   "source": [
+    "def sigmoidfn(x):\n",
+    "  y=1/(1+np.exp(-x))\n",
+    "  return y\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "t7KBbl7N3AUM"
+   },
+   "source": [
+    "##### (ii) Write a fn to implement derivative of sigmoid fn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "id": "okjuOpba3KOJ"
+   },
+   "outputs": [],
+   "source": [
+    "def derivative(x):\n",
+    " a=np.exp(x)\n",
+    " b=a/((1+np.exp(x))*(1+np.exp(x)))\n",
+    " return b"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "NdGTSpsp3mwr"
+   },
+   "source": [
+    "##### (iii) Test your code"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "lWY8RBex7gnd"
+   },
+   "source": [
+    "Run the following cell to check if your functions are working properly.\n",
+    "\n",
+    "*Expected output:*\n",
+    "\n",
+    "x on applying sigmoid activation fn is: [ [0.99987661 0.88079708 0.99330715 0.5        0.5       ]\n",
+    " [0.99908895 0.99330715 0.5        0.5        0.5       ] ]\n",
+    "\n",
+    "x on applying derivative of sigmoid activation fn is: [ [-1.23379350e-04 -1.04993585e-01 -6.64805667e-03 -2.50000000e-01\n",
+    "  -2.50000000e-01]\n",
+    " [-9.10221180e-04 -6.64805667e-03 -2.50000000e-01 -2.50000000e-01\n",
+    "  -2.50000000e-01] ]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "id": "ssDGpmS33vdA"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "x on applying sigmoid activation fn is: [[0.99987661 0.88079708 0.99330715 0.5        0.5       ]\n",
+      " [0.99908895 0.99330715 0.5        0.5        0.5       ]]\n",
+      "x on applying derivative of sigmoid activation fn is: [[1.23379350e-04 1.04993585e-01 6.64805667e-03 2.50000000e-01\n",
+      "  2.50000000e-01]\n",
+      " [9.10221180e-04 6.64805667e-03 2.50000000e-01 2.50000000e-01\n",
+      "  2.50000000e-01]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "x = np.array([\n",
+    "    [9,2,5,0,0],\n",
+    "    [7,5,0,0,0]\n",
+    "])\n",
+    "print(f\"x on applying sigmoid activation fn is: {sigmoidfn(x)}\")\n",
+    "print(f\"x on applying derivative of sigmoid activation fn is: {derivative(x)}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "PMa0cseyZpa1"
+   },
+   "source": [
+    "# Section 2: Exploring Pandas"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*You have been provided with a dataset which includes information about properties of superheated vapor.*\n",
+    "\n",
+    "*The dataset consists of the thermophysical properties: specific volume, specific internal energy, specific enthalpy, specific entropy of superheated vapor.*\n",
+    "\n",
+    "*Pressure is in kPa and Temperature in centigrade. In the dataframe 75, 100, 125, etc. are temperatures.*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "i5Okb_jxZ_VW"
+   },
+   "source": [
+    "### Read the csv file\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "id": "aQgvyavrcM1l"
+   },
+   "outputs": [
+    {
+     "ename": "FileNotFoundError",
+     "evalue": "[Errno 2] No such file or directory: 'superheated_vapor_properties.csv'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[13], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m df\u001b[38;5;241m=\u001b[39mpd\u001b[38;5;241m.\u001b[39mread_csv(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124msuperheated_vapor_properties.csv\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
+      "File \u001b[1;32mC:\\ProgramData\\anaconda3\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:912\u001b[0m, in \u001b[0;36mread_csv\u001b[1;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, date_format, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options, dtype_backend)\u001b[0m\n\u001b[0;32m    899\u001b[0m kwds_defaults \u001b[38;5;241m=\u001b[39m _refine_defaults_read(\n\u001b[0;32m    900\u001b[0m     dialect,\n\u001b[0;32m    901\u001b[0m     delimiter,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    908\u001b[0m     dtype_backend\u001b[38;5;241m=\u001b[39mdtype_backend,\n\u001b[0;32m    909\u001b[0m )\n\u001b[0;32m    910\u001b[0m kwds\u001b[38;5;241m.\u001b[39mupdate(kwds_defaults)\n\u001b[1;32m--> 912\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _read(filepath_or_buffer, kwds)\n",
+      "File \u001b[1;32mC:\\ProgramData\\anaconda3\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:577\u001b[0m, in \u001b[0;36m_read\u001b[1;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[0;32m    574\u001b[0m _validate_names(kwds\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnames\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m))\n\u001b[0;32m    576\u001b[0m \u001b[38;5;66;03m# Create the parser.\u001b[39;00m\n\u001b[1;32m--> 577\u001b[0m parser \u001b[38;5;241m=\u001b[39m TextFileReader(filepath_or_buffer, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwds)\n\u001b[0;32m    579\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m chunksize \u001b[38;5;129;01mor\u001b[39;00m iterator:\n\u001b[0;32m    580\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m parser\n",
+      "File \u001b[1;32mC:\\ProgramData\\anaconda3\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:1407\u001b[0m, in \u001b[0;36mTextFileReader.__init__\u001b[1;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[0;32m   1404\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m kwds[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[0;32m   1406\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles: IOHandles \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m-> 1407\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_make_engine(f, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mengine)\n",
+      "File \u001b[1;32mC:\\ProgramData\\anaconda3\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:1661\u001b[0m, in \u001b[0;36mTextFileReader._make_engine\u001b[1;34m(self, f, engine)\u001b[0m\n\u001b[0;32m   1659\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m mode:\n\u001b[0;32m   1660\u001b[0m         mode \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m-> 1661\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles \u001b[38;5;241m=\u001b[39m get_handle(\n\u001b[0;32m   1662\u001b[0m     f,\n\u001b[0;32m   1663\u001b[0m     mode,\n\u001b[0;32m   1664\u001b[0m     encoding\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mencoding\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m),\n\u001b[0;32m   1665\u001b[0m     compression\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcompression\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m),\n\u001b[0;32m   1666\u001b[0m     memory_map\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmemory_map\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mFalse\u001b[39;00m),\n\u001b[0;32m   1667\u001b[0m     is_text\u001b[38;5;241m=\u001b[39mis_text,\n\u001b[0;32m   1668\u001b[0m     errors\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mencoding_errors\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstrict\u001b[39m\u001b[38;5;124m\"\u001b[39m),\n\u001b[0;32m   1669\u001b[0m     storage_options\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstorage_options\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m),\n\u001b[0;32m   1670\u001b[0m )\n\u001b[0;32m   1671\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m   1672\u001b[0m f \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles\u001b[38;5;241m.\u001b[39mhandle\n",
+      "File \u001b[1;32mC:\\ProgramData\\anaconda3\\Lib\\site-packages\\pandas\\io\\common.py:859\u001b[0m, in \u001b[0;36mget_handle\u001b[1;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[0;32m    854\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(handle, \u001b[38;5;28mstr\u001b[39m):\n\u001b[0;32m    855\u001b[0m     \u001b[38;5;66;03m# Check whether the filename is to be opened in binary mode.\u001b[39;00m\n\u001b[0;32m    856\u001b[0m     \u001b[38;5;66;03m# Binary mode does not support 'encoding' and 'newline'.\u001b[39;00m\n\u001b[0;32m    857\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m ioargs\u001b[38;5;241m.\u001b[39mencoding \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m ioargs\u001b[38;5;241m.\u001b[39mmode:\n\u001b[0;32m    858\u001b[0m         \u001b[38;5;66;03m# Encoding\u001b[39;00m\n\u001b[1;32m--> 859\u001b[0m         handle \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mopen\u001b[39m(\n\u001b[0;32m    860\u001b[0m             handle,\n\u001b[0;32m    861\u001b[0m             ioargs\u001b[38;5;241m.\u001b[39mmode,\n\u001b[0;32m    862\u001b[0m             encoding\u001b[38;5;241m=\u001b[39mioargs\u001b[38;5;241m.\u001b[39mencoding,\n\u001b[0;32m    863\u001b[0m             errors\u001b[38;5;241m=\u001b[39merrors,\n\u001b[0;32m    864\u001b[0m             newline\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m    865\u001b[0m         )\n\u001b[0;32m    866\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    867\u001b[0m         \u001b[38;5;66;03m# Binary mode\u001b[39;00m\n\u001b[0;32m    868\u001b[0m         handle \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mopen\u001b[39m(handle, ioargs\u001b[38;5;241m.\u001b[39mmode)\n",
+      "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'superheated_vapor_properties.csv'"
+     ]
+    }
+   ],
+   "source": [
+    "df=pd.read_csv('superheated_vapor_properties.csv')\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Zp8F3rk2cNxy"
+   },
+   "source": [
+    "### Display the shape of data frame\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "id": "v3Q8kQkucgK0"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(544, 37)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "w49cp-8zcgd2"
+   },
+   "source": [
+    "### Return an array containing names of all the columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "id": "fz4HRb3JcsZp"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Index(['Pressure', 'Property', 'Liq_Sat', 'Vap_Sat', '75', '100', '125', '150',\n",
+      "       '175', '200', '220', '225', '240', '250', '260', '275', '280', '290',\n",
+      "       '300', '320', '325', '340', '350', '360', '375', '380', '400', '425',\n",
+      "       '450', '475', '500', '525', '550', '575', '600', '625', '650'],\n",
+      "      dtype='object')\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df.columns)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "YkTH9tRWcrH-"
+   },
+   "source": [
+    "### Display the number of null values in each column of the dataframe\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "id": "GK9-PJPxc3Ot"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Pressure      0\n",
+       "Property      0\n",
+       "Liq_Sat       0\n",
+       "Vap_Sat       0\n",
+       "75          528\n",
+       "100         508\n",
+       "125         488\n",
+       "150         448\n",
+       "175         384\n",
+       "200         320\n",
+       "220         408\n",
+       "225         400\n",
+       "240         408\n",
+       "250         344\n",
+       "260         384\n",
+       "275         340\n",
+       "280         380\n",
+       "290         488\n",
+       "300          60\n",
+       "320         480\n",
+       "325         136\n",
+       "340         476\n",
+       "350          68\n",
+       "360         476\n",
+       "375         204\n",
+       "380         476\n",
+       "400           0\n",
+       "425         204\n",
+       "450           0\n",
+       "475         204\n",
+       "500           0\n",
+       "525         272\n",
+       "550           0\n",
+       "575         340\n",
+       "600           0\n",
+       "625         476\n",
+       "650           0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.isnull().sum()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Av-lcft2c3mx"
+   },
+   "source": [
+    "### Create a column which contains the Pressure and Property columns, seperated with 'at' (For eg. V at 1, H at 101.325). Using this print the following:\n",
+    "- Enthalpy at 75 kPa and 573 K\n",
+    "- Entropy at 493 K and 250 kPa\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "metadata": {
+    "id": "B2AihQj_c32C"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Enthalpy at 75 KPa and 573K\n",
+      "75.0 at H\n",
+      "Entropy at 493 K and 250 kPa\n",
+      "250.0 at S\n"
+     ]
+    }
+   ],
+   "source": [
+    "df['Propert at Pressure']=df['Pressure'].astype(int).astype(str)+' at ' +df['Property'].astype(str)\n",
+    "print(\"Enthalpy at 75 KPa and 573K\")\n",
+    "print(df[(df['Pressure']==75)&(df['Property']=='H')]['Property at Pressure'].values[0])\n",
+    "print(\"Entropy at 493 K and 250 kPa\")\n",
+    "print(df[(df['Pressure']==250)&(df['Property']=='S')]['Property at Pressure'].values[0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "GpLtXYRLc4Ho"
+   },
+   "source": [
+    "### Find out the column with the highest number of missing values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "id": "q80Grgeyc4Xn"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Column with highest null value is 75\n"
+     ]
+    }
+   ],
+   "source": [
+    "b=df.isnull().sum()\n",
+    "max=0\n",
+    "r=df.shape[1]\n",
+    "for x in range(0,r):\n",
+    "    c=b[x]\n",
+    "    if c>max:\n",
+    "        max=c\n",
+    "        index=x\n",
+    "        \n",
+    "col=df.columns        \n",
+    "print(f\"Column with highest null value is {col[index]}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "74NVHTTac4nv"
+   },
+   "source": [
+    "### What is the average enthalpy of Sat. Liq. at all different pressures in the dataset?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "id": "g3mByhBDdpVr"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "936.9707720588235"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1=df[(df['Property']=='H')]\n",
+    "df1['Liq_Sat'].mean()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "uS-SuqU8dpxg"
+   },
+   "source": [
+    "### Separate out the V,U,H,S data from the dataset into V_data, U_data, H_data, S_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "id": "Sg3V9Toyd1Av"
+   },
+   "outputs": [],
+   "source": [
+    "V_data=df[df['Property']=='V']\n",
+    "U_data=df[df['Property']=='U']\n",
+    "H_data=df[df['Property']=='H']\n",
+    "S_data=df[df['Property']=='S']\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Ghkj0-0-d1W7"
+   },
+   "source": [
+    "# Section 3: Plotting with Matplotlib"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "ka0qrDcZeAL6"
+   },
+   "source": [
+    "### Plot the properties (specific volume, specific internal energy, specific enthalpy, specific entropy) vs Pressure for saturated liquid.\n",
+    "\n",
+    "Note:\n",
+    "- Try using the subplot feature of matplotlib(Explore it!!)\n",
+    "- Provide appropriate title, labels, markersize and other parameters to the plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "id": "XbKdm-cUePKA"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Entropy')"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from matplotlib import pyplot as pp\n",
+    "pp.subplot(2,2,1)\n",
+    "pp.plot(V_data['Pressure'],V_data['Liq_Sat'])\n",
+    "pp.xlabel(\"Pressure\")\n",
+    "pp.ylabel(\"Volume\")\n",
+    "pp.subplot(2,2,2)\n",
+    "pp.plot(U_data['Pressure'],U_data['Liq_Sat'])\n",
+    "pp.xlabel(\"Pressure\")\n",
+    "pp.ylabel(\"Internal Energy\")\n",
+    "pp.subplot(2,2,3)\n",
+    "pp.plot(H_data['Pressure'],H_data['Liq_Sat'])\n",
+    "pp.xlabel(\"Pressure\")\n",
+    "pp.ylabel(\"Enthalpy\")\n",
+    "pp.subplot(2,2,4)\n",
+    "pp.plot(S_data['Pressure'],S_data['Liq_Sat'])\n",
+    "pp.xlabel(\"Pressure\")\n",
+    "pp.ylabel(\"Entropy\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Plot the same for saturated vapor."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Entropy')"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
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",
+      "text/plain": [
+       "<Figure size 640x480 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "pp.subplot(2,2,1)\n",
+    "pp.plot(V_data['Pressure'],V_data['Vap_Sat'])\n",
+    "pp.xlabel(\"Pressure\")\n",
+    "pp.ylabel(\"Volume\")\n",
+    "pp.subplot(2,2,2)\n",
+    "pp.plot(U_data['Pressure'],U_data['Vap_Sat'])\n",
+    "pp.xlabel(\"Pressure\")\n",
+    "pp.ylabel(\"Internal Energy\")\n",
+    "pp.subplot(2,2,3)\n",
+    "pp.plot(H_data['Pressure'],H_data['Vap_Sat'])\n",
+    "pp.xlabel(\"Pressure\")\n",
+    "pp.ylabel(\"Enthalpy\")\n",
+    "pp.subplot(2,2,4)\n",
+    "pp.plot(S_data['Pressure'],S_data['Vap_Sat'])\n",
+    "pp.xlabel(\"Pressure\")\n",
+    "pp.ylabel(\"Entropy\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "SCLRji6TeQgh"
+   },
+   "source": [
+    "### Plot the specific volume of saturated liquid between 300 kPa and 1500 kPa"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "id": "u6DyyI7MeYgE"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'volume of saturated liquid between 300 kPa and 1500 kPa')"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "gn=V_data[(V_data['Pressure']>=300)&(V_data['Pressure']<=1500)]\n",
+    "pp.plot(gn['Liq_Sat'],gn['Pressure'])\n",
+    "pp.xlabel(\"Liq_Sat\")\n",
+    "pp.ylabel(\"Pressure\")\n",
+    "pp.title(\"volume of saturated liquid between 300 kPa and 1500 kPa\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Section 4 : Conclusion"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*Congratulations on reaching this point! I hope you had fun solving your first assignment and have also built confidence in applying these libraries. If you are wondering, we will cover more about z-score normalization in Week 2, and the sigmoid function will be used in Week 3. After completing this assignment, you are now prepared to learn about machine learning techniques and implement your own machine learning models.*"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "provenance": [],
+   "toc_visible": true
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/Assignment1.ipynb b/Assignment1.ipynb
index ab3ea13..fb07513 100644
--- a/Assignment1.ipynb
+++ b/Assignment1.ipynb
@@ -31,7 +31,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 10,
    "metadata": {
     "id": "jdwX7bI7aGeY"
    },
@@ -517,11 +517,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 13,
    "metadata": {
     "id": "aQgvyavrcM1l"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "FileNotFoundError",
+     "evalue": "[Errno 2] No such file or directory: 'superheated_vapor_properties.csv'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[13], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m df\u001b[38;5;241m=\u001b[39mpd\u001b[38;5;241m.\u001b[39mread_csv(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124msuperheated_vapor_properties.csv\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
+      "File \u001b[1;32mC:\\ProgramData\\anaconda3\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:912\u001b[0m, in \u001b[0;36mread_csv\u001b[1;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, date_format, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options, dtype_backend)\u001b[0m\n\u001b[0;32m    899\u001b[0m kwds_defaults \u001b[38;5;241m=\u001b[39m _refine_defaults_read(\n\u001b[0;32m    900\u001b[0m     dialect,\n\u001b[0;32m    901\u001b[0m     delimiter,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    908\u001b[0m     dtype_backend\u001b[38;5;241m=\u001b[39mdtype_backend,\n\u001b[0;32m    909\u001b[0m )\n\u001b[0;32m    910\u001b[0m kwds\u001b[38;5;241m.\u001b[39mupdate(kwds_defaults)\n\u001b[1;32m--> 912\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _read(filepath_or_buffer, kwds)\n",
+      "File \u001b[1;32mC:\\ProgramData\\anaconda3\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:577\u001b[0m, in \u001b[0;36m_read\u001b[1;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[0;32m    574\u001b[0m _validate_names(kwds\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnames\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m))\n\u001b[0;32m    576\u001b[0m \u001b[38;5;66;03m# Create the parser.\u001b[39;00m\n\u001b[1;32m--> 577\u001b[0m parser \u001b[38;5;241m=\u001b[39m TextFileReader(filepath_or_buffer, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwds)\n\u001b[0;32m    579\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m chunksize \u001b[38;5;129;01mor\u001b[39;00m iterator:\n\u001b[0;32m    580\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m parser\n",
+      "File \u001b[1;32mC:\\ProgramData\\anaconda3\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:1407\u001b[0m, in \u001b[0;36mTextFileReader.__init__\u001b[1;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[0;32m   1404\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m kwds[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[0;32m   1406\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles: IOHandles \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m-> 1407\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_make_engine(f, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mengine)\n",
+      "File \u001b[1;32mC:\\ProgramData\\anaconda3\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:1661\u001b[0m, in \u001b[0;36mTextFileReader._make_engine\u001b[1;34m(self, f, engine)\u001b[0m\n\u001b[0;32m   1659\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m mode:\n\u001b[0;32m   1660\u001b[0m         mode \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m-> 1661\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles \u001b[38;5;241m=\u001b[39m get_handle(\n\u001b[0;32m   1662\u001b[0m     f,\n\u001b[0;32m   1663\u001b[0m     mode,\n\u001b[0;32m   1664\u001b[0m     encoding\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mencoding\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m),\n\u001b[0;32m   1665\u001b[0m     compression\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcompression\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m),\n\u001b[0;32m   1666\u001b[0m     memory_map\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmemory_map\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mFalse\u001b[39;00m),\n\u001b[0;32m   1667\u001b[0m     is_text\u001b[38;5;241m=\u001b[39mis_text,\n\u001b[0;32m   1668\u001b[0m     errors\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mencoding_errors\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstrict\u001b[39m\u001b[38;5;124m\"\u001b[39m),\n\u001b[0;32m   1669\u001b[0m     storage_options\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstorage_options\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m),\n\u001b[0;32m   1670\u001b[0m )\n\u001b[0;32m   1671\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m   1672\u001b[0m f \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles\u001b[38;5;241m.\u001b[39mhandle\n",
+      "File \u001b[1;32mC:\\ProgramData\\anaconda3\\Lib\\site-packages\\pandas\\io\\common.py:859\u001b[0m, in \u001b[0;36mget_handle\u001b[1;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[0;32m    854\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(handle, \u001b[38;5;28mstr\u001b[39m):\n\u001b[0;32m    855\u001b[0m     \u001b[38;5;66;03m# Check whether the filename is to be opened in binary mode.\u001b[39;00m\n\u001b[0;32m    856\u001b[0m     \u001b[38;5;66;03m# Binary mode does not support 'encoding' and 'newline'.\u001b[39;00m\n\u001b[0;32m    857\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m ioargs\u001b[38;5;241m.\u001b[39mencoding \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m ioargs\u001b[38;5;241m.\u001b[39mmode:\n\u001b[0;32m    858\u001b[0m         \u001b[38;5;66;03m# Encoding\u001b[39;00m\n\u001b[1;32m--> 859\u001b[0m         handle \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mopen\u001b[39m(\n\u001b[0;32m    860\u001b[0m             handle,\n\u001b[0;32m    861\u001b[0m             ioargs\u001b[38;5;241m.\u001b[39mmode,\n\u001b[0;32m    862\u001b[0m             encoding\u001b[38;5;241m=\u001b[39mioargs\u001b[38;5;241m.\u001b[39mencoding,\n\u001b[0;32m    863\u001b[0m             errors\u001b[38;5;241m=\u001b[39merrors,\n\u001b[0;32m    864\u001b[0m             newline\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m    865\u001b[0m         )\n\u001b[0;32m    866\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    867\u001b[0m         \u001b[38;5;66;03m# Binary mode\u001b[39;00m\n\u001b[0;32m    868\u001b[0m         handle \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mopen\u001b[39m(handle, ioargs\u001b[38;5;241m.\u001b[39mmode)\n",
+      "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'superheated_vapor_properties.csv'"
+     ]
+    }
+   ],
    "source": [
     "df=pd.read_csv('superheated_vapor_properties.csv')\n"
    ]
diff --git a/Assignments/Assignment 1/Assignment1.ipynb b/Assignments/Assignment 1/Assignment1.ipynb
index ab3ea13..d5a7596 100644
--- a/Assignments/Assignment 1/Assignment1.ipynb	
+++ b/Assignments/Assignment 1/Assignment1.ipynb	
@@ -31,7 +31,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 1,
    "metadata": {
     "id": "jdwX7bI7aGeY"
    },
@@ -517,13 +517,389 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 2,
    "metadata": {
     "id": "aQgvyavrcM1l"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Pressure</th>\n",
+       "      <th>Property</th>\n",
+       "      <th>Liq_Sat</th>\n",
+       "      <th>Vap_Sat</th>\n",
+       "      <th>75</th>\n",
+       "      <th>100</th>\n",
+       "      <th>125</th>\n",
+       "      <th>150</th>\n",
+       "      <th>175</th>\n",
+       "      <th>200</th>\n",
+       "      <th>...</th>\n",
+       "      <th>425</th>\n",
+       "      <th>450</th>\n",
+       "      <th>475</th>\n",
+       "      <th>500</th>\n",
+       "      <th>525</th>\n",
+       "      <th>550</th>\n",
+       "      <th>575</th>\n",
+       "      <th>600</th>\n",
+       "      <th>625</th>\n",
+       "      <th>650</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>V</td>\n",
+       "      <td>1.0000</td>\n",
+       "      <td>129200.0000</td>\n",
+       "      <td>160640.0000</td>\n",
+       "      <td>172180.0000</td>\n",
+       "      <td>183720.0000</td>\n",
+       "      <td>195270.0000</td>\n",
+       "      <td>206810.0000</td>\n",
+       "      <td>218350.0000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>333730.0000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>356810.0000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>379880.0000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>402960.0000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>426040.0000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>U</td>\n",
+       "      <td>29.3340</td>\n",
+       "      <td>2385.2000</td>\n",
+       "      <td>2480.8000</td>\n",
+       "      <td>2516.4000</td>\n",
+       "      <td>2552.3000</td>\n",
+       "      <td>2588.5000</td>\n",
+       "      <td>2624.9000</td>\n",
+       "      <td>2661.7000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3049.9000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3132.4000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3216.7000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3302.6000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3390.3000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>H</td>\n",
+       "      <td>29.3350</td>\n",
+       "      <td>2514.4000</td>\n",
+       "      <td>2641.5000</td>\n",
+       "      <td>2688.6000</td>\n",
+       "      <td>2736.0000</td>\n",
+       "      <td>2783.7000</td>\n",
+       "      <td>2831.7000</td>\n",
+       "      <td>2880.1000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3383.6000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3489.2000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3596.5000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3705.6000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3816.4000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>S</td>\n",
+       "      <td>0.1060</td>\n",
+       "      <td>8.9767</td>\n",
+       "      <td>9.3828</td>\n",
+       "      <td>9.5136</td>\n",
+       "      <td>9.6365</td>\n",
+       "      <td>9.7527</td>\n",
+       "      <td>9.8629</td>\n",
+       "      <td>9.9679</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>10.8200</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>10.9612</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11.0957</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11.2243</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11.3476</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>V</td>\n",
+       "      <td>1.0100</td>\n",
+       "      <td>14670.0000</td>\n",
+       "      <td>16030.0000</td>\n",
+       "      <td>17190.0000</td>\n",
+       "      <td>18350.0000</td>\n",
+       "      <td>19510.0000</td>\n",
+       "      <td>20660.0000</td>\n",
+       "      <td>21820.0000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>33370.0000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>35670.0000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>37980.0000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>40290.0000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>42600.0000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>539</th>\n",
+       "      <td>11200.0</td>\n",
+       "      <td>S</td>\n",
+       "      <td>3.4440</td>\n",
+       "      <td>5.5476</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>6.2491</td>\n",
+       "      <td>6.3515</td>\n",
+       "      <td>6.4452</td>\n",
+       "      <td>6.5324</td>\n",
+       "      <td>6.6147</td>\n",
+       "      <td>6.6929</td>\n",
+       "      <td>6.7679</td>\n",
+       "      <td>6.8401</td>\n",
+       "      <td>6.9099</td>\n",
+       "      <td>6.9777</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>540</th>\n",
+       "      <td>11400.0</td>\n",
+       "      <td>V</td>\n",
+       "      <td>1.5040</td>\n",
+       "      <td>15.2840</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>24.1040</td>\n",
+       "      <td>25.5990</td>\n",
+       "      <td>27.0100</td>\n",
+       "      <td>28.3590</td>\n",
+       "      <td>29.6610</td>\n",
+       "      <td>30.9250</td>\n",
+       "      <td>32.1600</td>\n",
+       "      <td>33.3700</td>\n",
+       "      <td>34.5600</td>\n",
+       "      <td>35.7330</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>541</th>\n",
+       "      <td>11400.0</td>\n",
+       "      <td>U</td>\n",
+       "      <td>1450.0000</td>\n",
+       "      <td>2527.2000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2873.1000</td>\n",
+       "      <td>2929.4000</td>\n",
+       "      <td>2982.6000</td>\n",
+       "      <td>3033.9000</td>\n",
+       "      <td>3083.9000</td>\n",
+       "      <td>3133.1000</td>\n",
+       "      <td>3181.9000</td>\n",
+       "      <td>3230.4000</td>\n",
+       "      <td>3278.8000</td>\n",
+       "      <td>3327.2000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>542</th>\n",
+       "      <td>11400.0</td>\n",
+       "      <td>H</td>\n",
+       "      <td>1467.2000</td>\n",
+       "      <td>2701.5000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3147.9000</td>\n",
+       "      <td>3221.2000</td>\n",
+       "      <td>3290.5000</td>\n",
+       "      <td>3357.2000</td>\n",
+       "      <td>3422.1000</td>\n",
+       "      <td>3485.7000</td>\n",
+       "      <td>3548.5000</td>\n",
+       "      <td>3610.8000</td>\n",
+       "      <td>3672.8000</td>\n",
+       "      <td>3734.6000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>543</th>\n",
+       "      <td>11400.0</td>\n",
+       "      <td>S</td>\n",
+       "      <td>3.4575</td>\n",
+       "      <td>5.5357</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>6.2367</td>\n",
+       "      <td>6.3399</td>\n",
+       "      <td>6.4341</td>\n",
+       "      <td>6.5218</td>\n",
+       "      <td>6.6043</td>\n",
+       "      <td>6.6828</td>\n",
+       "      <td>6.7580</td>\n",
+       "      <td>6.8304</td>\n",
+       "      <td>6.9004</td>\n",
+       "      <td>6.9683</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>544 rows × 37 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Pressure Property    Liq_Sat      Vap_Sat           75          100  \\\n",
+       "0         1.0        V     1.0000  129200.0000  160640.0000  172180.0000   \n",
+       "1         1.0        U    29.3340    2385.2000    2480.8000    2516.4000   \n",
+       "2         1.0        H    29.3350    2514.4000    2641.5000    2688.6000   \n",
+       "3         1.0        S     0.1060       8.9767       9.3828       9.5136   \n",
+       "4        10.0        V     1.0100   14670.0000   16030.0000   17190.0000   \n",
+       "..        ...      ...        ...          ...          ...          ...   \n",
+       "539   11200.0        S     3.4440       5.5476          NaN          NaN   \n",
+       "540   11400.0        V     1.5040      15.2840          NaN          NaN   \n",
+       "541   11400.0        U  1450.0000    2527.2000          NaN          NaN   \n",
+       "542   11400.0        H  1467.2000    2701.5000          NaN          NaN   \n",
+       "543   11400.0        S     3.4575       5.5357          NaN          NaN   \n",
+       "\n",
+       "             125          150          175          200  ...        425  \\\n",
+       "0    183720.0000  195270.0000  206810.0000  218350.0000  ...        NaN   \n",
+       "1      2552.3000    2588.5000    2624.9000    2661.7000  ...        NaN   \n",
+       "2      2736.0000    2783.7000    2831.7000    2880.1000  ...        NaN   \n",
+       "3         9.6365       9.7527       9.8629       9.9679  ...        NaN   \n",
+       "4     18350.0000   19510.0000   20660.0000   21820.0000  ...        NaN   \n",
+       "..           ...          ...          ...          ...  ...        ...   \n",
+       "539          NaN          NaN          NaN          NaN  ...     6.2491   \n",
+       "540          NaN          NaN          NaN          NaN  ...    24.1040   \n",
+       "541          NaN          NaN          NaN          NaN  ...  2873.1000   \n",
+       "542          NaN          NaN          NaN          NaN  ...  3147.9000   \n",
+       "543          NaN          NaN          NaN          NaN  ...     6.2367   \n",
+       "\n",
+       "             450        475          500        525          550        575  \\\n",
+       "0    333730.0000        NaN  356810.0000        NaN  379880.0000        NaN   \n",
+       "1      3049.9000        NaN    3132.4000        NaN    3216.7000        NaN   \n",
+       "2      3383.6000        NaN    3489.2000        NaN    3596.5000        NaN   \n",
+       "3        10.8200        NaN      10.9612        NaN      11.0957        NaN   \n",
+       "4     33370.0000        NaN   35670.0000        NaN   37980.0000        NaN   \n",
+       "..           ...        ...          ...        ...          ...        ...   \n",
+       "539       6.3515     6.4452       6.5324     6.6147       6.6929     6.7679   \n",
+       "540      25.5990    27.0100      28.3590    29.6610      30.9250    32.1600   \n",
+       "541    2929.4000  2982.6000    3033.9000  3083.9000    3133.1000  3181.9000   \n",
+       "542    3221.2000  3290.5000    3357.2000  3422.1000    3485.7000  3548.5000   \n",
+       "543       6.3399     6.4341       6.5218     6.6043       6.6828     6.7580   \n",
+       "\n",
+       "             600        625          650  \n",
+       "0    402960.0000        NaN  426040.0000  \n",
+       "1      3302.6000        NaN    3390.3000  \n",
+       "2      3705.6000        NaN    3816.4000  \n",
+       "3        11.2243        NaN      11.3476  \n",
+       "4     40290.0000        NaN   42600.0000  \n",
+       "..           ...        ...          ...  \n",
+       "539       6.8401     6.9099       6.9777  \n",
+       "540      33.3700    34.5600      35.7330  \n",
+       "541    3230.4000  3278.8000    3327.2000  \n",
+       "542    3610.8000  3672.8000    3734.6000  \n",
+       "543       6.8304     6.9004       6.9683  \n",
+       "\n",
+       "[544 rows x 37 columns]"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "df=pd.read_csv('superheated_vapor_properties.csv')\n"
+    "df=pd.read_csv('superheated_vapor_properties.csv')\n",
+    "df"
    ]
   },
   {
@@ -537,7 +913,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 31,
    "metadata": {
     "id": "v3Q8kQkucgK0"
    },
@@ -565,7 +941,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 22,
    "metadata": {
     "id": "fz4HRb3JcsZp"
    },
@@ -598,7 +974,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 23,
    "metadata": {
     "id": "GK9-PJPxc3Ot"
    },
@@ -646,7 +1022,7 @@
        "dtype: int64"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -669,7 +1045,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 80,
+   "execution_count": 37,
    "metadata": {
     "id": "B2AihQj_c32C"
    },
@@ -679,18 +1055,393 @@
      "output_type": "stream",
      "text": [
       "Enthalpy at 75 KPa and 573K\n",
-      "75.0 at H\n",
+      "75 at H\n",
       "Entropy at 493 K and 250 kPa\n",
-      "250.0 at S\n"
+      "250 at S\n"
      ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Pressure</th>\n",
+       "      <th>Property</th>\n",
+       "      <th>Liq_Sat</th>\n",
+       "      <th>Vap_Sat</th>\n",
+       "      <th>75</th>\n",
+       "      <th>100</th>\n",
+       "      <th>125</th>\n",
+       "      <th>150</th>\n",
+       "      <th>175</th>\n",
+       "      <th>200</th>\n",
+       "      <th>...</th>\n",
+       "      <th>475</th>\n",
+       "      <th>500</th>\n",
+       "      <th>525</th>\n",
+       "      <th>550</th>\n",
+       "      <th>575</th>\n",
+       "      <th>600</th>\n",
+       "      <th>625</th>\n",
+       "      <th>650</th>\n",
+       "      <th>Propert at Pressure</th>\n",
+       "      <th>Property at Pressure</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>V</td>\n",
+       "      <td>1.0000</td>\n",
+       "      <td>129200.0000</td>\n",
+       "      <td>160640.0000</td>\n",
+       "      <td>172180.0000</td>\n",
+       "      <td>183720.0000</td>\n",
+       "      <td>195270.0000</td>\n",
+       "      <td>206810.0000</td>\n",
+       "      <td>218350.0000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>356810.0000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>379880.0000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>402960.0000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>426040.0000</td>\n",
+       "      <td>1 at V</td>\n",
+       "      <td>1 at V</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>U</td>\n",
+       "      <td>29.3340</td>\n",
+       "      <td>2385.2000</td>\n",
+       "      <td>2480.8000</td>\n",
+       "      <td>2516.4000</td>\n",
+       "      <td>2552.3000</td>\n",
+       "      <td>2588.5000</td>\n",
+       "      <td>2624.9000</td>\n",
+       "      <td>2661.7000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3132.4000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3216.7000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3302.6000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3390.3000</td>\n",
+       "      <td>1 at U</td>\n",
+       "      <td>1 at U</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>H</td>\n",
+       "      <td>29.3350</td>\n",
+       "      <td>2514.4000</td>\n",
+       "      <td>2641.5000</td>\n",
+       "      <td>2688.6000</td>\n",
+       "      <td>2736.0000</td>\n",
+       "      <td>2783.7000</td>\n",
+       "      <td>2831.7000</td>\n",
+       "      <td>2880.1000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3489.2000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3596.5000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3705.6000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3816.4000</td>\n",
+       "      <td>1 at H</td>\n",
+       "      <td>1 at H</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>S</td>\n",
+       "      <td>0.1060</td>\n",
+       "      <td>8.9767</td>\n",
+       "      <td>9.3828</td>\n",
+       "      <td>9.5136</td>\n",
+       "      <td>9.6365</td>\n",
+       "      <td>9.7527</td>\n",
+       "      <td>9.8629</td>\n",
+       "      <td>9.9679</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>10.9612</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11.0957</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11.2243</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11.3476</td>\n",
+       "      <td>1 at S</td>\n",
+       "      <td>1 at S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>V</td>\n",
+       "      <td>1.0100</td>\n",
+       "      <td>14670.0000</td>\n",
+       "      <td>16030.0000</td>\n",
+       "      <td>17190.0000</td>\n",
+       "      <td>18350.0000</td>\n",
+       "      <td>19510.0000</td>\n",
+       "      <td>20660.0000</td>\n",
+       "      <td>21820.0000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>35670.0000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>37980.0000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>40290.0000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>42600.0000</td>\n",
+       "      <td>10 at V</td>\n",
+       "      <td>10 at V</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>539</th>\n",
+       "      <td>11200.0</td>\n",
+       "      <td>S</td>\n",
+       "      <td>3.4440</td>\n",
+       "      <td>5.5476</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>6.4452</td>\n",
+       "      <td>6.5324</td>\n",
+       "      <td>6.6147</td>\n",
+       "      <td>6.6929</td>\n",
+       "      <td>6.7679</td>\n",
+       "      <td>6.8401</td>\n",
+       "      <td>6.9099</td>\n",
+       "      <td>6.9777</td>\n",
+       "      <td>11200 at S</td>\n",
+       "      <td>11200 at S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>540</th>\n",
+       "      <td>11400.0</td>\n",
+       "      <td>V</td>\n",
+       "      <td>1.5040</td>\n",
+       "      <td>15.2840</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>27.0100</td>\n",
+       "      <td>28.3590</td>\n",
+       "      <td>29.6610</td>\n",
+       "      <td>30.9250</td>\n",
+       "      <td>32.1600</td>\n",
+       "      <td>33.3700</td>\n",
+       "      <td>34.5600</td>\n",
+       "      <td>35.7330</td>\n",
+       "      <td>11400 at V</td>\n",
+       "      <td>11400 at V</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>541</th>\n",
+       "      <td>11400.0</td>\n",
+       "      <td>U</td>\n",
+       "      <td>1450.0000</td>\n",
+       "      <td>2527.2000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2982.6000</td>\n",
+       "      <td>3033.9000</td>\n",
+       "      <td>3083.9000</td>\n",
+       "      <td>3133.1000</td>\n",
+       "      <td>3181.9000</td>\n",
+       "      <td>3230.4000</td>\n",
+       "      <td>3278.8000</td>\n",
+       "      <td>3327.2000</td>\n",
+       "      <td>11400 at U</td>\n",
+       "      <td>11400 at U</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>542</th>\n",
+       "      <td>11400.0</td>\n",
+       "      <td>H</td>\n",
+       "      <td>1467.2000</td>\n",
+       "      <td>2701.5000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3290.5000</td>\n",
+       "      <td>3357.2000</td>\n",
+       "      <td>3422.1000</td>\n",
+       "      <td>3485.7000</td>\n",
+       "      <td>3548.5000</td>\n",
+       "      <td>3610.8000</td>\n",
+       "      <td>3672.8000</td>\n",
+       "      <td>3734.6000</td>\n",
+       "      <td>11400 at H</td>\n",
+       "      <td>11400 at H</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>543</th>\n",
+       "      <td>11400.0</td>\n",
+       "      <td>S</td>\n",
+       "      <td>3.4575</td>\n",
+       "      <td>5.5357</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>6.4341</td>\n",
+       "      <td>6.5218</td>\n",
+       "      <td>6.6043</td>\n",
+       "      <td>6.6828</td>\n",
+       "      <td>6.7580</td>\n",
+       "      <td>6.8304</td>\n",
+       "      <td>6.9004</td>\n",
+       "      <td>6.9683</td>\n",
+       "      <td>11400 at S</td>\n",
+       "      <td>11400 at S</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>544 rows × 39 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Pressure Property    Liq_Sat      Vap_Sat           75          100  \\\n",
+       "0         1.0        V     1.0000  129200.0000  160640.0000  172180.0000   \n",
+       "1         1.0        U    29.3340    2385.2000    2480.8000    2516.4000   \n",
+       "2         1.0        H    29.3350    2514.4000    2641.5000    2688.6000   \n",
+       "3         1.0        S     0.1060       8.9767       9.3828       9.5136   \n",
+       "4        10.0        V     1.0100   14670.0000   16030.0000   17190.0000   \n",
+       "..        ...      ...        ...          ...          ...          ...   \n",
+       "539   11200.0        S     3.4440       5.5476          NaN          NaN   \n",
+       "540   11400.0        V     1.5040      15.2840          NaN          NaN   \n",
+       "541   11400.0        U  1450.0000    2527.2000          NaN          NaN   \n",
+       "542   11400.0        H  1467.2000    2701.5000          NaN          NaN   \n",
+       "543   11400.0        S     3.4575       5.5357          NaN          NaN   \n",
+       "\n",
+       "             125          150          175          200  ...        475  \\\n",
+       "0    183720.0000  195270.0000  206810.0000  218350.0000  ...        NaN   \n",
+       "1      2552.3000    2588.5000    2624.9000    2661.7000  ...        NaN   \n",
+       "2      2736.0000    2783.7000    2831.7000    2880.1000  ...        NaN   \n",
+       "3         9.6365       9.7527       9.8629       9.9679  ...        NaN   \n",
+       "4     18350.0000   19510.0000   20660.0000   21820.0000  ...        NaN   \n",
+       "..           ...          ...          ...          ...  ...        ...   \n",
+       "539          NaN          NaN          NaN          NaN  ...     6.4452   \n",
+       "540          NaN          NaN          NaN          NaN  ...    27.0100   \n",
+       "541          NaN          NaN          NaN          NaN  ...  2982.6000   \n",
+       "542          NaN          NaN          NaN          NaN  ...  3290.5000   \n",
+       "543          NaN          NaN          NaN          NaN  ...     6.4341   \n",
+       "\n",
+       "             500        525          550        575          600        625  \\\n",
+       "0    356810.0000        NaN  379880.0000        NaN  402960.0000        NaN   \n",
+       "1      3132.4000        NaN    3216.7000        NaN    3302.6000        NaN   \n",
+       "2      3489.2000        NaN    3596.5000        NaN    3705.6000        NaN   \n",
+       "3        10.9612        NaN      11.0957        NaN      11.2243        NaN   \n",
+       "4     35670.0000        NaN   37980.0000        NaN   40290.0000        NaN   \n",
+       "..           ...        ...          ...        ...          ...        ...   \n",
+       "539       6.5324     6.6147       6.6929     6.7679       6.8401     6.9099   \n",
+       "540      28.3590    29.6610      30.9250    32.1600      33.3700    34.5600   \n",
+       "541    3033.9000  3083.9000    3133.1000  3181.9000    3230.4000  3278.8000   \n",
+       "542    3357.2000  3422.1000    3485.7000  3548.5000    3610.8000  3672.8000   \n",
+       "543       6.5218     6.6043       6.6828     6.7580       6.8304     6.9004   \n",
+       "\n",
+       "             650  Propert at Pressure  Property at Pressure  \n",
+       "0    426040.0000               1 at V                1 at V  \n",
+       "1      3390.3000               1 at U                1 at U  \n",
+       "2      3816.4000               1 at H                1 at H  \n",
+       "3        11.3476               1 at S                1 at S  \n",
+       "4     42600.0000              10 at V               10 at V  \n",
+       "..           ...                  ...                   ...  \n",
+       "539       6.9777           11200 at S            11200 at S  \n",
+       "540      35.7330           11400 at V            11400 at V  \n",
+       "541    3327.2000           11400 at U            11400 at U  \n",
+       "542    3734.6000           11400 at H            11400 at H  \n",
+       "543       6.9683           11400 at S            11400 at S  \n",
+       "\n",
+       "[544 rows x 39 columns]"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "df['Propert at Pressure']=df['Pressure'].astype(int).astype(str)+' at ' +df['Property'].astype(str)\n",
+    "df['Property at Pressure']=df['Pressure'].astype(int).astype(str)+' at ' +df['Property'].astype(str)\n",
     "print(\"Enthalpy at 75 KPa and 573K\")\n",
     "print(df[(df['Pressure']==75)&(df['Property']=='H')]['Property at Pressure'].values[0])\n",
     "print(\"Entropy at 493 K and 250 kPa\")\n",
-    "print(df[(df['Pressure']==250)&(df['Property']=='S')]['Property at Pressure'].values[0])"
+    "print(df[(df['Pressure']==250)&(df['Property']=='S')]['Property at Pressure'].values[0])\n",
+    "df"
    ]
   },
   {
@@ -742,25 +1493,377 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 7,
    "metadata": {
     "id": "g3mByhBDdpVr"
    },
    "outputs": [
     {
      "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Pressure</th>\n",
+       "      <th>Property</th>\n",
+       "      <th>Liq_Sat</th>\n",
+       "      <th>Vap_Sat</th>\n",
+       "      <th>75</th>\n",
+       "      <th>100</th>\n",
+       "      <th>125</th>\n",
+       "      <th>150</th>\n",
+       "      <th>175</th>\n",
+       "      <th>200</th>\n",
+       "      <th>...</th>\n",
+       "      <th>425</th>\n",
+       "      <th>450</th>\n",
+       "      <th>475</th>\n",
+       "      <th>500</th>\n",
+       "      <th>525</th>\n",
+       "      <th>550</th>\n",
+       "      <th>575</th>\n",
+       "      <th>600</th>\n",
+       "      <th>625</th>\n",
+       "      <th>650</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>H</td>\n",
+       "      <td>29.335</td>\n",
+       "      <td>2514.4</td>\n",
+       "      <td>2641.5</td>\n",
+       "      <td>2688.6</td>\n",
+       "      <td>2736.0</td>\n",
+       "      <td>2783.7</td>\n",
+       "      <td>2831.7</td>\n",
+       "      <td>2880.1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3383.6</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3489.2</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3596.5</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3705.6</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3816.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>H</td>\n",
+       "      <td>191.832</td>\n",
+       "      <td>2584.8</td>\n",
+       "      <td>2640.0</td>\n",
+       "      <td>2687.5</td>\n",
+       "      <td>2735.2</td>\n",
+       "      <td>2783.1</td>\n",
+       "      <td>2831.2</td>\n",
+       "      <td>2879.6</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3383.5</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3489.1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3596.5</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3705.5</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3816.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>20.0</td>\n",
+       "      <td>H</td>\n",
+       "      <td>251.453</td>\n",
+       "      <td>2609.9</td>\n",
+       "      <td>2638.4</td>\n",
+       "      <td>2686.3</td>\n",
+       "      <td>2734.2</td>\n",
+       "      <td>2782.3</td>\n",
+       "      <td>2830.6</td>\n",
+       "      <td>2879.2</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3383.4</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3489.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3596.4</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3705.4</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3816.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>30.0</td>\n",
+       "      <td>H</td>\n",
+       "      <td>289.302</td>\n",
+       "      <td>2625.4</td>\n",
+       "      <td>2636.8</td>\n",
+       "      <td>2685.1</td>\n",
+       "      <td>2733.3</td>\n",
+       "      <td>2781.6</td>\n",
+       "      <td>2830.0</td>\n",
+       "      <td>2878.7</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3383.3</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3488.9</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3596.3</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3705.4</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3816.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>40.0</td>\n",
+       "      <td>H</td>\n",
+       "      <td>317.650</td>\n",
+       "      <td>2636.9</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2683.8</td>\n",
+       "      <td>2732.3</td>\n",
+       "      <td>2780.9</td>\n",
+       "      <td>2829.5</td>\n",
+       "      <td>2878.2</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3383.1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3488.8</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3596.2</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3705.3</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3816.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>526</th>\n",
+       "      <td>10600.0</td>\n",
+       "      <td>H</td>\n",
+       "      <td>1433.700</td>\n",
+       "      <td>2716.9</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3163.0</td>\n",
+       "      <td>3234.1</td>\n",
+       "      <td>3301.8</td>\n",
+       "      <td>3367.2</td>\n",
+       "      <td>3431.0</td>\n",
+       "      <td>3493.8</td>\n",
+       "      <td>3555.9</td>\n",
+       "      <td>3617.6</td>\n",
+       "      <td>3679.1</td>\n",
+       "      <td>3740.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>530</th>\n",
+       "      <td>10800.0</td>\n",
+       "      <td>H</td>\n",
+       "      <td>1442.200</td>\n",
+       "      <td>2713.1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3159.3</td>\n",
+       "      <td>3230.9</td>\n",
+       "      <td>3299.0</td>\n",
+       "      <td>3364.7</td>\n",
+       "      <td>3428.8</td>\n",
+       "      <td>3491.8</td>\n",
+       "      <td>3554.1</td>\n",
+       "      <td>3615.9</td>\n",
+       "      <td>3677.5</td>\n",
+       "      <td>3738.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>534</th>\n",
+       "      <td>11000.0</td>\n",
+       "      <td>H</td>\n",
+       "      <td>1450.600</td>\n",
+       "      <td>2709.3</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3155.5</td>\n",
+       "      <td>3227.7</td>\n",
+       "      <td>3296.2</td>\n",
+       "      <td>3362.2</td>\n",
+       "      <td>3426.5</td>\n",
+       "      <td>3489.7</td>\n",
+       "      <td>3552.2</td>\n",
+       "      <td>3614.2</td>\n",
+       "      <td>3675.9</td>\n",
+       "      <td>3737.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>538</th>\n",
+       "      <td>11200.0</td>\n",
+       "      <td>H</td>\n",
+       "      <td>1458.900</td>\n",
+       "      <td>2705.4</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3151.7</td>\n",
+       "      <td>3224.5</td>\n",
+       "      <td>3293.4</td>\n",
+       "      <td>3359.7</td>\n",
+       "      <td>3424.3</td>\n",
+       "      <td>3487.7</td>\n",
+       "      <td>3550.4</td>\n",
+       "      <td>3612.5</td>\n",
+       "      <td>3674.4</td>\n",
+       "      <td>3736.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>542</th>\n",
+       "      <td>11400.0</td>\n",
+       "      <td>H</td>\n",
+       "      <td>1467.200</td>\n",
+       "      <td>2701.5</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3147.9</td>\n",
+       "      <td>3221.2</td>\n",
+       "      <td>3290.5</td>\n",
+       "      <td>3357.2</td>\n",
+       "      <td>3422.1</td>\n",
+       "      <td>3485.7</td>\n",
+       "      <td>3548.5</td>\n",
+       "      <td>3610.8</td>\n",
+       "      <td>3672.8</td>\n",
+       "      <td>3734.6</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>136 rows × 37 columns</p>\n",
+       "</div>"
+      ],
       "text/plain": [
-       "936.9707720588235"
+       "     Pressure Property   Liq_Sat  Vap_Sat      75     100     125     150  \\\n",
+       "2         1.0        H    29.335   2514.4  2641.5  2688.6  2736.0  2783.7   \n",
+       "6        10.0        H   191.832   2584.8  2640.0  2687.5  2735.2  2783.1   \n",
+       "10       20.0        H   251.453   2609.9  2638.4  2686.3  2734.2  2782.3   \n",
+       "14       30.0        H   289.302   2625.4  2636.8  2685.1  2733.3  2781.6   \n",
+       "18       40.0        H   317.650   2636.9     NaN  2683.8  2732.3  2780.9   \n",
+       "..        ...      ...       ...      ...     ...     ...     ...     ...   \n",
+       "526   10600.0        H  1433.700   2716.9     NaN     NaN     NaN     NaN   \n",
+       "530   10800.0        H  1442.200   2713.1     NaN     NaN     NaN     NaN   \n",
+       "534   11000.0        H  1450.600   2709.3     NaN     NaN     NaN     NaN   \n",
+       "538   11200.0        H  1458.900   2705.4     NaN     NaN     NaN     NaN   \n",
+       "542   11400.0        H  1467.200   2701.5     NaN     NaN     NaN     NaN   \n",
+       "\n",
+       "        175     200  ...     425     450     475     500     525     550  \\\n",
+       "2    2831.7  2880.1  ...     NaN  3383.6     NaN  3489.2     NaN  3596.5   \n",
+       "6    2831.2  2879.6  ...     NaN  3383.5     NaN  3489.1     NaN  3596.5   \n",
+       "10   2830.6  2879.2  ...     NaN  3383.4     NaN  3489.0     NaN  3596.4   \n",
+       "14   2830.0  2878.7  ...     NaN  3383.3     NaN  3488.9     NaN  3596.3   \n",
+       "18   2829.5  2878.2  ...     NaN  3383.1     NaN  3488.8     NaN  3596.2   \n",
+       "..      ...     ...  ...     ...     ...     ...     ...     ...     ...   \n",
+       "526     NaN     NaN  ...  3163.0  3234.1  3301.8  3367.2  3431.0  3493.8   \n",
+       "530     NaN     NaN  ...  3159.3  3230.9  3299.0  3364.7  3428.8  3491.8   \n",
+       "534     NaN     NaN  ...  3155.5  3227.7  3296.2  3362.2  3426.5  3489.7   \n",
+       "538     NaN     NaN  ...  3151.7  3224.5  3293.4  3359.7  3424.3  3487.7   \n",
+       "542     NaN     NaN  ...  3147.9  3221.2  3290.5  3357.2  3422.1  3485.7   \n",
+       "\n",
+       "        575     600     625     650  \n",
+       "2       NaN  3705.6     NaN  3816.4  \n",
+       "6       NaN  3705.5     NaN  3816.3  \n",
+       "10      NaN  3705.4     NaN  3816.2  \n",
+       "14      NaN  3705.4     NaN  3816.2  \n",
+       "18      NaN  3705.3     NaN  3816.1  \n",
+       "..      ...     ...     ...     ...  \n",
+       "526  3555.9  3617.6  3679.1  3740.4  \n",
+       "530  3554.1  3615.9  3677.5  3738.9  \n",
+       "534  3552.2  3614.2  3675.9  3737.5  \n",
+       "538  3550.4  3612.5  3674.4  3736.0  \n",
+       "542  3548.5  3610.8  3672.8  3734.6  \n",
+       "\n",
+       "[136 rows x 37 columns]"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "df1=df[(df['Property']=='H')]\n",
-    "df1['Liq_Sat'].mean()"
+    "df1=df[df['Property']=='H']\n",
+    "df1['Liq_Sat'].mean()\n",
+    "df1\n"
    ]
   },
   {
diff --git a/Copy of AirQualityUCI.csv b/Copy of AirQualityUCI.csv
new file mode 100644
index 0000000..f6fecf5
--- /dev/null
+++ b/Copy of AirQualityUCI.csv	
@@ -0,0 +1,9358 @@
+Date,Time,CO(GT),PT08.S1(CO),NMHC(GT),C6H6(GT),PT08.S2(NMHC),NOx(GT),PT08.S3(NOx),NO2(GT),PT08.S4(NO2),PT08.S5(O3),T,RH,AH
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diff --git a/Lectures/Lecture 2 - Pandas & Matplotlib.ipynb b/Lectures/Lecture 2 - Pandas & Matplotlib.ipynb
index bf16795..05b2273 100644
--- a/Lectures/Lecture 2 - Pandas & Matplotlib.ipynb	
+++ b/Lectures/Lecture 2 - Pandas & Matplotlib.ipynb	
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 4,
    "id": "bff31180",
    "metadata": {},
    "outputs": [],
@@ -28,7 +28,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 106,
    "id": "ac0481aa",
    "metadata": {},
    "outputs": [],
@@ -46,7 +46,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 67,
    "id": "6e36d3fd",
    "metadata": {},
    "outputs": [
@@ -262,7 +262,7 @@
        "[208 rows x 10 columns]"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 67,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -407,7 +407,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 68,
    "id": "804588af",
    "metadata": {},
    "outputs": [
@@ -605,7 +605,7 @@
        "9  Carter Bays,Craig Thomas    12.27          9.2   4667  "
       ]
      },
-     "execution_count": 5,
+     "execution_count": 68,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -616,7 +616,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 69,
    "id": "6c914e81",
    "metadata": {},
    "outputs": [
@@ -739,7 +739,7 @@
        "207  Carter Bays,Craig Thomas    13.13          5.5  15760  "
       ]
      },
-     "execution_count": 6,
+     "execution_count": 69,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -750,7 +750,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 70,
    "id": "ada7d342",
    "metadata": {},
    "outputs": [
@@ -948,7 +948,7 @@
        "207  Carter Bays,Craig Thomas    13.13          5.5  15760  "
       ]
      },
-     "execution_count": 7,
+     "execution_count": 70,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -967,7 +967,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 71,
    "id": "7b2777b4",
    "metadata": {},
    "outputs": [
@@ -979,7 +979,7 @@
        "      dtype='object')"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 71,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -990,7 +990,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 72,
    "id": "a53d755b",
    "metadata": {},
    "outputs": [
@@ -1010,7 +1010,7 @@
        "dtype: object"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 72,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1021,7 +1021,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 73,
    "id": "639ee9b3",
    "metadata": {},
    "outputs": [
@@ -1153,7 +1153,7 @@
        "max    15760.000000  "
       ]
      },
-     "execution_count": 10,
+     "execution_count": 73,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1164,7 +1164,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 74,
    "id": "79c1ca58",
    "metadata": {},
    "outputs": [
@@ -1268,7 +1268,7 @@
        "max    208.000000    9.000000   24.000000  15760.000000"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 74,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1287,7 +1287,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 75,
    "id": "11706b59",
    "metadata": {},
    "outputs": [
@@ -1302,7 +1302,7 @@
        "Name: Viewers, dtype: float64"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 75,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1576,7 +1576,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 65,
    "id": "53dc7d02",
    "metadata": {},
    "outputs": [
@@ -1602,8 +1602,6 @@
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>SNo</th>\n",
-       "      <th>Season</th>\n",
-       "      <th>Episode</th>\n",
        "      <th>DateAired</th>\n",
        "      <th>Title</th>\n",
        "      <th>Director</th>\n",
@@ -1617,8 +1615,6 @@
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>5</td>\n",
-       "      <td>1</td>\n",
-       "      <td>5</td>\n",
        "      <td>17-Oct-05</td>\n",
        "      <td>Okay Awesome</td>\n",
        "      <td>Pamela Fryman</td>\n",
@@ -1630,8 +1626,6 @@
        "    <tr>\n",
        "      <th>14</th>\n",
        "      <td>15</td>\n",
-       "      <td>1</td>\n",
-       "      <td>15</td>\n",
        "      <td>27-Feb-06</td>\n",
        "      <td>Game Night</td>\n",
        "      <td>Pamela Fryman</td>\n",
@@ -1643,8 +1637,6 @@
        "    <tr>\n",
        "      <th>18</th>\n",
        "      <td>19</td>\n",
-       "      <td>1</td>\n",
-       "      <td>19</td>\n",
        "      <td>24-Apr-06</td>\n",
        "      <td>Mary the Paralegal</td>\n",
        "      <td>Pamela Fryman</td>\n",
@@ -1656,8 +1648,6 @@
        "    <tr>\n",
        "      <th>23</th>\n",
        "      <td>24</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
        "      <td>25-Sep-06</td>\n",
        "      <td>The Scorpion and the Toad</td>\n",
        "      <td>Rob Greenberg</td>\n",
@@ -1669,8 +1659,6 @@
        "    <tr>\n",
        "      <th>38</th>\n",
        "      <td>39</td>\n",
-       "      <td>2</td>\n",
-       "      <td>17</td>\n",
        "      <td>26-Feb-07</td>\n",
        "      <td>Arrivederci, Fiero</td>\n",
        "      <td>Pamela Fryman</td>\n",
@@ -1684,22 +1672,22 @@
        "</div>"
       ],
       "text/plain": [
-       "    SNo  Season  Episode  DateAired                      Title       Director  \\\n",
-       "4     5       1        5  17-Oct-05               Okay Awesome  Pamela Fryman   \n",
-       "14   15       1       15  27-Feb-06                 Game Night  Pamela Fryman   \n",
-       "18   19       1       19  24-Apr-06         Mary the Paralegal  Pamela Fryman   \n",
-       "23   24       2        2  25-Sep-06  The Scorpion and the Toad  Rob Greenberg   \n",
-       "38   39       2       17  26-Feb-07         Arrivederci, Fiero  Pamela Fryman   \n",
+       "    SNo  DateAired                      Title       Director       Writers  \\\n",
+       "4     5  17-Oct-05               Okay Awesome  Pamela Fryman  Chris Harris   \n",
+       "14   15  27-Feb-06                 Game Night  Pamela Fryman  Chris Harris   \n",
+       "18   19  24-Apr-06         Mary the Paralegal  Pamela Fryman  Chris Harris   \n",
+       "23   24  25-Sep-06  The Scorpion and the Toad  Rob Greenberg  Chris Harris   \n",
+       "38   39  26-Feb-07         Arrivederci, Fiero  Pamela Fryman  Chris Harris   \n",
        "\n",
-       "         Writers  Viewers  IMDB_Rating  Votes  \n",
-       "4   Chris Harris    10.14          8.3   3412  \n",
-       "14  Chris Harris     9.82          9.1   3780  \n",
-       "18  Chris Harris     7.60          8.8   3337  \n",
-       "23  Chris Harris     9.14          8.2   2725  \n",
-       "38  Chris Harris     9.33          8.3   2775  "
+       "    Viewers  IMDB_Rating  Votes  \n",
+       "4     10.14          8.3   3412  \n",
+       "14     9.82          9.1   3780  \n",
+       "18     7.60          8.8   3337  \n",
+       "23     9.14          8.2   2725  \n",
+       "38     9.33          8.3   2775  "
       ]
      },
-     "execution_count": 16,
+     "execution_count": 65,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2161,7 +2149,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 115,
    "id": "2409682e",
    "metadata": {},
    "outputs": [
@@ -2284,7 +2272,7 @@
        "4              Chris Harris    10.14          8.3   3412  "
       ]
      },
-     "execution_count": 26,
+     "execution_count": 115,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2297,131 +2285,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 134,
    "id": "a7d024ce",
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>SNo</th>\n",
-       "      <th>Season</th>\n",
-       "      <th>DateAired</th>\n",
-       "      <th>Title</th>\n",
-       "      <th>Director</th>\n",
-       "      <th>Writers</th>\n",
-       "      <th>Viewers</th>\n",
-       "      <th>IMDB_Rating</th>\n",
-       "      <th>Votes</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>19-Sep-05</td>\n",
-       "      <td>Pilot</td>\n",
-       "      <td>Pamela Fryman</td>\n",
-       "      <td>Carter Bays,Craig Thomas</td>\n",
-       "      <td>10.94</td>\n",
-       "      <td>8.5</td>\n",
-       "      <td>4935</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>26-Sep-05</td>\n",
-       "      <td>Purple Giraffe</td>\n",
-       "      <td>Pamela Fryman</td>\n",
-       "      <td>Carter Bays,Craig Thomas</td>\n",
-       "      <td>10.40</td>\n",
-       "      <td>8.2</td>\n",
-       "      <td>3708</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>3</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3-Oct-05</td>\n",
-       "      <td>Sweet Taste of Liberty</td>\n",
-       "      <td>Pamela Fryman</td>\n",
-       "      <td>Phil Lord,Chris Miller</td>\n",
-       "      <td>10.44</td>\n",
-       "      <td>8.2</td>\n",
-       "      <td>3514</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>4</td>\n",
-       "      <td>1</td>\n",
-       "      <td>10-Oct-05</td>\n",
-       "      <td>Return of the Shirt</td>\n",
-       "      <td>Pamela Fryman</td>\n",
-       "      <td>Kourtney Kang</td>\n",
-       "      <td>9.84</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>3298</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>5</td>\n",
-       "      <td>1</td>\n",
-       "      <td>17-Oct-05</td>\n",
-       "      <td>Okay Awesome</td>\n",
-       "      <td>Pamela Fryman</td>\n",
-       "      <td>Chris Harris</td>\n",
-       "      <td>10.14</td>\n",
-       "      <td>8.3</td>\n",
-       "      <td>3412</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   SNo  Season  DateAired                   Title       Director  \\\n",
-       "0    1       1  19-Sep-05                   Pilot  Pamela Fryman   \n",
-       "1    2       1  26-Sep-05          Purple Giraffe  Pamela Fryman   \n",
-       "2    3       1   3-Oct-05  Sweet Taste of Liberty  Pamela Fryman   \n",
-       "3    4       1  10-Oct-05     Return of the Shirt  Pamela Fryman   \n",
-       "4    5       1  17-Oct-05            Okay Awesome  Pamela Fryman   \n",
-       "\n",
-       "                    Writers  Viewers  IMDB_Rating  Votes  \n",
-       "0  Carter Bays,Craig Thomas    10.94          8.5   4935  \n",
-       "1  Carter Bays,Craig Thomas    10.40          8.2   3708  \n",
-       "2    Phil Lord,Chris Miller    10.44          8.2   3514  \n",
-       "3             Kourtney Kang     9.84          8.0   3298  \n",
-       "4              Chris Harris    10.14          8.3   3412  "
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
+     "ename": "SyntaxError",
+     "evalue": "closing parenthesis ')' does not match opening parenthesis '[' (776923485.py, line 1)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;36m  Cell \u001b[1;32mIn[134], line 1\u001b[1;36m\u001b[0m\n\u001b[1;33m    df2 = df.drop([df.index[1],df.index[2])\u001b[0m\n\u001b[1;37m                                          ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m closing parenthesis ')' does not match opening parenthesis '['\n"
+     ]
     }
    ],
    "source": [
-    "df = df.drop('Episode',axis=1)\n",
-    "df.head()"
+    "df2 = df.drop([df.index[1],df.index[2])\n",
+    "df2.head()"
    ]
   },
   {
@@ -2508,7 +2387,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 7,
    "id": "ceed1893",
    "metadata": {},
    "outputs": [
@@ -2517,7 +2396,7 @@
      "output_type": "stream",
      "text": [
       "<class 'numpy.ndarray'>\n",
-      "(203, 3)\n"
+      "(208, 3)\n"
      ]
     }
    ],
diff --git a/Q1.ipynb b/Q1.ipynb
new file mode 100644
index 0000000..67f44fd
--- /dev/null
+++ b/Q1.ipynb
@@ -0,0 +1,466 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 752,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as  np \n",
+    "import pandas as pd \n",
+    "from matplotlib import pyplot as plt \n",
+    "import math"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "GENARATING DATA SET"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 754,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "50.18286570443969"
+      ]
+     },
+     "execution_count": 754,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rng=np.random.RandomState(10)\n",
+    "x=2*rng.rand(50)\n",
+    "y=5+2*x+rng.randn(50)\n",
+    "m=x.shape[0] \n",
+    "np.sum(x)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "PLOT OF DATA"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 755,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(x,y,\"bo\")\n",
+    "plt.xlim(0,2)\n",
+    "plt.ylim(0,15)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "COST FUNCTION 1 for linear regression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 813,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def cost1(x_out,x):\n",
+    "    cf1=(abs((x-x_out))**3)/2\n",
+    "    cff1=cf1.mean()\n",
+    "    return cff1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "COST FUNCTION 2 for polynomial regression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 681,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def cost2(y_out,y):\n",
+    "    cf2=(abs((y-y_out)**7)/2)\n",
+    "    cff2=cf2.mean()\n",
+    "    return cff2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Hypothesis 1 for linear regression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 757,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def hyp1(w,b,y):\n",
+    "    return w*y+b"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Hypothesis 2 for polynomial regression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 682,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def hyp2(a,c,x):\n",
+    "    return a*(np.log(x))+c\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Gradient Descent 1\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 833,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def gd1(x,y,w,b,m,learning_rate):\n",
+    "    x_out=hyp1(w,b,y)    \n",
+    "    dw=(3/(2*m))*np.sum(np.multiply(np.multiply(abs(x-x_out),(x-x_out)),-y))\n",
+    "    db=-(3/(2*m))*np.sum(np.multiply(abs(x-x_out),(x-x_out)))\n",
+    "    w=w-learning_rate*dw\n",
+    "    b=b-learning_rate*db\n",
+    "    return w,b"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Gradient Descent 2 for polynomial regression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 683,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def gd2(x,y,a,c,learning_rate,m):\n",
+    "    y_out=hyp2(a,c,x)\n",
+    "    x_log=np.log(x)\n",
+    "    da=(7/(2*m))*np.sum(np.multiply(np.multiply(abs(y-y_out)**5,(y-y_out)),-x_log))\n",
+    "    \n",
+    "    dc=(7/(2*m))*np.sum(np.multiply(np.multiply((abs(y-y_out)**5),(y-y_out)),-1))\n",
+    "    a=a-learning_rate*da\n",
+    "    \n",
+    "    c=c-learning_rate*dc\n",
+    "    \n",
+    "    return a,c\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Implement for linear regression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 834,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "w = np.random.randn(1)\n",
+    "b= np.random.randn(1)\n",
+    "\n",
+    "learning_rate=0.00005\n",
+    "necho=5000000\n",
+    "\n",
+    "\n",
+    "L=[]\n",
+    "for i in range(necho):\n",
+    "    w,b=gd1(x,y,w,b,m,learning_rate)\n",
+    "    x_out=hyp1(w,b,y)\n",
+    "    L.append(cost1(x_out,x))\n",
+    "    #if (i%(necho/10))==0:\n",
+    "       # plt.plot(x,y,'bo')\n",
+    "        #plt.plot(x_out,y,'g--')\n",
+    "        #plt.show()\n",
+    "        \n",
+    "        \n",
+    "        \n",
+    "        \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 830,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.0, 15.0)"
+      ]
+     },
+     "execution_count": 830,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(x,y,\"bo\")\n",
+    "plt.plot(x_out,y,\"r\")\n",
+    "plt.xlim(0,2)\n",
+    "plt.ylim(0,15)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 831,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.0, 0.1)"
+      ]
+     },
+     "execution_count": 831,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(L,\"r\")\n",
+    "plt.ylim(0,0.1)\n",
+    "#plt.xlim(0,1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "IMPLEMENT POLYNOMIAL REGRESSION"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 724,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a=np.random.randn(1)\n",
+    "B=np.random.randn(1)\n",
+    "c=np.random.randn(1)\n",
+    "d=np.random.randn(1)\n",
+    "\n",
+    "learning_rate=0.000005\n",
+    "noche=1000000\n",
+    "\n",
+    "L1=[]\n",
+    "\n",
+    "\n",
+    "for i in range(noche):\n",
+    "    a,c= gd2(x,y,a,c,learning_rate,m)\n",
+    "    y_out=hyp2(a,c,x)\n",
+    "    L1.append(cost2(y_out,y))\n",
+    "    \n",
+    "    #if (i%(necho/10))==0:\n",
+    "       # plt.plot(x,y,'bo')\n",
+    "        #plt.plot(x_out,y,'g--')\n",
+    "        #plt.show()\n",
+    "       "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 809,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.0, 11.0)"
+      ]
+     },
+     "execution_count": 809,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(x,y,\"bo\")\n",
+    "plt.plot(x,y_out,\"go\")\n",
+    "plt.ylim(0,11)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 726,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.0, 70.0)"
+      ]
+     },
+     "execution_count": 726,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(L1,\"r\")\n",
+    "plt.ylim(0,70)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "USING SCIKIT"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 832,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x2a814999340>]"
+      ]
+     },
+     "execution_count": 832,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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BeapXYsvC6WsFVHfTLFdeuYKYUjEq9ZRQUOAo7rZ5s6P9ppvkKMeAATI5WIRgbRciIgMKtF6Jy3E6Xd9h6IJuRbkr7la6tKO4W7t2hsnN4Y0/92+OfBARGZAaWTj1GngYsqBbUUIA69c7irtZrbK9Rg1Hcbfq1bXto8aMu2eKiCiClSQL56bsTS6Bx1O3PKWbwMNQBd2KysuTAUeTJkCXLsCKFTLw6NRJJgjLygLGjYv4wAPgyAcRkSEFmoXT3WhH7ku5qBCj/TS3oQq6FfW//8m1HB995Cjudt11juJuTZpo2z8dYvBBRGRA/tYrAfQ7zWKj+4JuRRUWyimV2bPlFItN48aO4m5ct+gRgw8iIgPyNwun3gMPQMcF3Yo6edJR3C0nR7ZFRcnhmBEjgM6dw2YBaTBxzQcRkUH5Uq9kz+k9LoHHnQ3u1F3gAeisoFtRQgCbNgEDB8qiLuPGycCjShXg5ZflWo6MDENVldUat9oSEflIr9s/PfWr/sz6OHjuoOLYY6OPIaF8qO/evrFtH/Y2leRt+7BqLl2SicBmzwZ+/dXR3ratnFrp1w+IYS4UG261JSJSmZ63f7rLwmmEaRZnmhZ0K+rAAUdxt/PnZVtsrBz5GD4cuPnmIHcg/HHahYjIC6Nt/zRi4GHjy1RSUFgswKpVQK9eMr351Kky8KhbF3j7bfnif/ABAw+VcNqFiKgYamUSDYVj+cdQY6ryrt2oUiP8MeKPoJ9b7SmpkE1xnTkjRzjmzJEvIiBf1J495ShHGBd3UxunXYiIVGKU7Z89PumBbw5+o2j7Y/gfaFS5kerncg4M/voLePZZdaekglLQrajt2+VajvR0ZXG3IUNkcbd69YJ4cmLwQURUDCNs/wzlNIu7tS/u+FvcLiQKCmSm0dmzgZ9/drS3aOEo7la2rGbdiyQMPoiIiqHb7Z9/C3Xg4a6Krju6ykh65IijuNvp07KtdGng3nvl1ErbttwiG2IMPoiIihFIJtFQOHv5LCq9VUnRFmWKgmW8JSjnKy71uSeaTkkJAXz/vay18sUXjuJuSUnAE08AQ4cC1aqFuFNkw+CDiKgYutn+WUSr+a2w4/gORdvPQ35Gm6Q2QTunt7UvxQnplFRenqyxMns28EeRhbZ33CFHOe66CyjFW5/W+AoQEXlh2/7pLs/H9OmhXdOg1TbakgQQIZmS+v13GXB8/LGyuNugQbK42403hqAT5CsGH0REPkhNlWsXtMxwqmX+jkACiKBPSV275ijulpnpaL/hBjnK8dBDLO6mUww+iIh8FPTtnx5cunYJ5d4o59IeysRh3ta+OAvqlNSJE47ibseOyTaz2VHcrVMnLiDVOQYfREQ6NmD5ACzZvUTR9l6f9zD05qEh7Udxa1/cUXNKymIBNv4gcG3DJjTfOBtVNy6D6do1+cmqVYHHHgOGDZNF38gQGHwQEemUu2kW63grTBq9q/e09iU5Gfj3v2WRV7WnpFYuvoifhi/GwPOz0QL/tbefadQWlcaPAO65h8XdDIjBBxGRDum1PkvI1r4cOID9z76Ljl8tQArOAwAuIxaLMRDvYjh27rsZy2KBVMYdhsTaLkREOmKxWlDqddf3hXoIPILOYgFWr5a5OdautTcfRF28i6ewAINxDvEA9FVThyR/7t+saktEpBNPf/20S+AxruO48A88zpyRlWMbNAD69AHWroUwmbAKd+JOrEID7MdUPGcPPABlAjMyHk67EBHpgLtplsJxhTBHhfHb+m3b5DbZJUscxd2uvx549FF8lfQk7nrWe3E3LWvqUOAYfBARaUyv6zuCoqAAWLpUBh1btjjaW7aUuTn+Lu5WPtO3p9Oqpg6VDIMPIiKNCCEQ9Zrr7HdYBh5Hjsi8HO+/ryzudt99Muj4xz8UuTn0WlOH1KH6mg+LxYJx48ahTp06KFOmDOrVq4fXX38dOlvXSkSkqWmbp7kEHt3qdguvwEMI4LvvgLvvBurUASZPloFHUhIwcaJctPHJJ26rytryigCu+cK0qqlD6lF95OPNN9/EnDlzsGjRIjRp0gTbtm3D4MGDERcXh2eeeUbt0xFRmLFYtE1hHgruplkujLmActGuWUwNKTfXUdxt715He5cucpSjTx+firvpqaYOqUv14GPTpk1ISUlB7969AQC1a9dGeno6fvnlF7VPRURhJiPD/Y1mxozwudGE9fqO3bsdxd0uXpRt5cs7irvdcIPfT6mHmjqkPtWDj3bt2mH+/PnYt28fGjZsiP/+97/48ccfMXXqVLfHFxQUoKCgwP44Ly9P7S4RkQFkZMjU3c4ztDk5sn3ZMuMHIGEZeFy7Bnz+uQw6NmxwtN94o6O4W/nyJTqFVjV1KHhUDz5eeukl5OXloXHjxjCbzbBYLJg0aRIeeOABt8dPnjwZaWlpaneDiAzEYpEjHu6Whgkh5/hHjZLvgI34jnfZ/5bh3s/uVbQllk9EzugcjXqkguPHgfnz5UfR4m59+8ribrffzuJu5JHqwcfSpUvx6aefYvHixWjSpAl+/fVXjBo1ComJiRg0aJDL8WPGjMHo0aPtj/Py8pDM4kBEEWXjRuVUi7OiCaWM9g7Y3WjHiedOoNp11TToTQkJAfz0k8xAunw5UFgo26tWBR5/XBZ3S0rSto9kCKoHH88//zxeeukl9O/fHwDQrFkzHD58GJMnT3YbfMTExCCGRYGIIpqviaKMllAqbKZZLl4EPv1UTq389pujvV07Ocpxzz1AdLR2/SPDUT34uHTpEqKilNvHzGYzrFar2qciojDha6IoIyWUCovAY/9+4N13gQUL5A4WAChTBnjgAbmeo0ULTbtHxqV68NGnTx9MmjQJNWvWRJMmTbBz505MnToVjz76qNqnIqIwEU4JpX468hNuW3CbS7thAg+LBfj6azm18s03jvZ69eSOlcGDZQp0ohJQPfj4v//7P4wbNw5PPfUUTp06hcTERAwbNgzjx49X+1REFCZsCaX69ZOBRtEAxEgJpdyNdvwx/A80qtxIg9746cwZ4IMPgDlzgEOHZJvJBNx5p5xa6d4diGItUlKHSegs9ag/JXmJKLy4y/ORnGyMhFJuC8ONFboPmOzF3dLTZd0VAIiPB4YMAZ54AqhbV9v+kWH4c/9m8EFEumLEDKfuAg+8KvSbIO3KFeCzz+TUStEEkDffLEc5+veXazuI/MDgg4goBPaf2Y+Gsxq6fuJV+WfVNmWkmwRphw87irv99Zdsi452FHdr04a5OShg/ty/WdWWiCgAbkc7PvwBOOJYFauLBGlWK7BunZxa+fJL+RiQ81lPPAEMHSrzdBCFEIMPIiI/eZpmcUezBGm5ucCiRTLo2LfP0d61qxzl+Oc/fSruRhoy4hykj/iTR0TkB38Cj6JCliBt1y4ZcHzyibK42yOPyK2yjRuHqCMRyDlYaNcO2LQpsOAhzKssMvggIvLBiQsnkPBv1yxn628X6OzD1wc1Qdq1a8CKFTLo+OEHR/uNN8oFpA8+WOLibmHD39EEX493FyyYzfLrbXwNHiKgyiIXnBIReRH9ejSuWa8p2hakLMAjLR6BxQLUru09QVpWVhBGzG3F3ebNcwytmM3A3XfLqRUfi7sZenTfn877O5rg6/GeggVnvqxAtv1AeSp2FNQfqJLhbhciIpX4kibddu8B3CdIU/WNqhDyZjt7tjyxrbhbtWqyuNvjj/tV3E23o/tFgwrbgthTp5T/379fBl85RaoDe+q8pwDB04vk6/HeggVn3oKHzEygsw9jaevX667Kol/3b6Ezubm5AoDIzc3VuitEFOHwKlw+PFm+XIikJCHk3Up+JCfLdlXk5wsxd64QzZopT9K+vRDp6UIUFPj9lMuXC2EyKZ8OkG0mkwp9LywUYv16IRYvlv8WFvreMedvpq8f7jpfWFj885lM8sWy9c+f49evD6yf69e7v/bFi337+sWLA39dgsSf+zfXfBAROblw9QLKT3ZdI1FcfZbUVLmdVvXpi337ZHG3hQtVLe5mscgRD3dj3163CNtGJXJygNOngSpVgOrV5edOnZIXf/o0MHq0/0Mqvk5heOKu8xs3Fj8y4bwlyZ/jA11J7OnrwrHKohsMPohIE3pdZ1B3Rl1knc9StI3vOB5pndO8fq3ZrNJIuMUCrFolp1aKFnerX1/uWHnkkZIVd7NYsOv/NuK2o8dxHAn4Ce3QHpuQAPl4IzrAKszutwi7m6fxlbcFk8VFRP5wDiZ8DRBsx/lzfKBBgKevC6cqi8Vg8EGkU3q9OatBr+sM3K3vsI63whSqrJ9//eUo7nb48N+dMgG9e8tRDk/F3QJYdNni6FGk/91UCDNKwbErIxtJGIkZWIFU5X04GKMSRXkbcfCXrfP+jib4c7y3YMGZt+AhXKosehOCaSC/cM0Hkfsp76QkFdcPaCjo6wwC5M/6DtX98osQDz8sREyM4xsSHy/ECy8I8eefymOd11EsXer7D4uHb77V6bEFJmGBSdyN5Y6lCd7WQaix5sHX9Q7+nsPWd3c/eMWt+fD1eNv31dPxgfyQB30Rkfr8uX8z+CDSGb3enNXgyzq+pCQhvvvO/zWKgbpmuRaawMM5aLhwQYiFC4W49VblN6FhQyE++ECIS5dcn8PXhZiBLLp0E4AcNSeLwoK/X4BAF1Z6+nC3YFKtczgHB7bvnbsAwdMvViDHO39/zeaSBQ+BLtjViD/3b261JdIRA2/x94mvuwiLCuZ0TN8lfbFy70pFW496PbDmwTW+P0mgW0Kjohx1VpyVJJeEjfMPSyDffMCxpTM9HRg40P+v9/a8RXlLmuKL4vY3u5vvS06W0xi+5vko7ng1M5waEPN8EBmUgbf4+ySQ+1ewKsO6W99xdexVlDaX9vxFzjcXdzs6VOlcCXNJFFXS4GHxYmDAgMCDF2feImhPSVN8VVxwAAQvwymxqm2k4++Kcfm7KN9oAtkYIIT6lWF9SRzmoiS7PPzlfNElWYjp76JLZ7avsy2sLMn1+7JgMjVVBl2+fq+TkoDHHgMaNPDtD56/W5JU28JERTH4CDN63UVAvgn3Lf7+bgywEcJLZVhPUx9ONyMhBKJec90t4lPgUZJdHoEoetEliTadg4dAd2UU3YUR6PchKan4UQkb56Qpnqaz+O7KsBh8ODHyqEEE1CIKe+G+xb+4XYS+cHsP9jYi8Xf0PbLMBsz8ZabLp70GHmrlnghUoLkkigsevH3zPY1Q+DMqkZwMTJ0KVK4c2B9UjjiENa75KMLIowbhvlAxkoS0TohGiosXSuEqhuNd1MNBHEQ9zMEwtMUWJOA4XpyWgBZPF7mB+TIiYTLBKgT63QesuNHRfO7Fc6gYW9F7Z9Va6xCo9evlTdufhZj+Lrp0rr7q67qJ4jKcGumdG6mCtV0CYPTtjb7uUPNUToD0xYBb/P1WWFAodk5bL34asVhse2e9qFmjULyJ58U1KLcnOuegsOew8GPrqAUQhytARI0PYBut2rknnD5crs/eHmAuCV9+WJy3cBYUGGpLJ+kTa7v4qUQ1DnQi3BcqRpqg1QnRi4wMmP/OsmlzIPY6lMIF719rm0d89VWfFz9GAaiZB3Q4DGQu9HOwN1gLbEwmiL//6FhhQhQc/bJCjlxs6T8d/7C96J6mPAKZ3nA3pcEpDgohBh/wv+aQHoX7QsVIFLZT3h6mSkpfuQABwHkfisu+FNs7gpmu6ze8yeyx2O+vQYcOckrhxAn/v7YYokYSHr88HWfOADMwEslw/BE6iiQ8i+nYuiQVWZOLxBFhH5VSpGDwgfAYNQj3hYoUZKFKjuRl8abPFVSEAM6c8f/8/kTfhYXAV1/J4m5qBB5OW0I3WDrg/a7ye7oSKeiAjcrCbjAD7t70hG1USpGEwQfCY9QgUmoRhYKRdzwFxJcFiGqtvFa7cFh8PHDunPdFmP5E36dPA++/D8ydCxw54vj6Vq2AQ4dk8Tcb5ymPIttALZWqYtcu4NKhUyhbLwHNnuoAc7TjB+l4uuNprDBjAzq57Y6e3/R4E3G/S+S74C9B8Y8WC079rSGkZ5GwUDGYwrmgm1ueVloHa+W12os309K8L8L0te9btgjx0ENCREc7vrZSJSFefFGIrCx5jI+1Nnz5OQr3ReIR97tELCwXCH9rCOmZwWoR6YbRdzz5zd8qpWpE4UEoHPb73NfFkQoB7vy4fFkWd7vlFuXX3HKLbL982e9L9PXnKJze9DiLuN8lEkKwsFzA/K0hROFDl3lSgj1mXdJCY4FQuXCYadc9AIAoq9zJknABmHj/fNSLr1t8volDh4A5c4APPnCsHYmJAe6/Hxg+HGjdukSX5+vPUTjmdNHl7xKFBGu7BIgLySOX7nY8hSLjXaCLCUqyCMHb4iQhiq/2CthTdNsCDwCwRgEb6qD4bKVWK/Dtt8CsWcCqVY5z16wJPPkkMGSITJZVAv7+HHnaPetrFnI90t3vEukSgw8nXEgemUK+46m4UY1Q5ckvaaGxQHm74/7zn8C77wIHDwL16gHDhgFbtii+V6aJrn+6PAYe584BCxfK5zxwwNHerZsc5fjnP1V7h+Hrz8e6dY6XPNze9ITD7kEKPk67ECHEpeyLG9VISQndmLW/UyBqj5cHMK206+QuNJ/b3KXdbeDx3//KbbKffgpcuiTbKlQAHnkEeOopoFGjkl+DE39msrQs3RDMGb2Q/i6RrjC9OpGfSrz4z9dVvt5W4qWlhXYLhK8pu3WwUhCvwuVjxZ4VyoMKCoRITxfittuU/W/aVIi5c4XIzw9qH739HOnhWxrsXSjhvJCWisfdLkQBCGjHU2GhDBji473/Nfe2u8Rkkls7fQk+Fi9W98Kd+2VW1lfRer+2u8BD4ehRIcaNE6J6dXufLeZS4mSn+0Th9xuEsFpD1ld/SrCE+kYcql0o4bR70B+RvtOQwQdRgIreh6NQKG7HejE8frHYkLbe9S/J8uWegwV3f2XV2maq5siHjY4LjXkMPKxW2bd+/RTB0omoBDEBE0QCclR/V+8rd/FcKF9Od3yJfdUMhCIt5xDzmnCrLVFg/p4It2bn4OTi7xC/8QvEXDzr+HzRSfqMDOCeezw/F+C6RiI9HRg40Hs/isvaGUH7FI/nH0fi1ESXdjE6D/jkE7me4/ff7e2Ha3XE84eHYwXuRiFK29uDvW3V0/oJi0XWvps40ftzLF4MDBigft+K0mItRqRkOPW0RtzIW6YDwa22RP4qsgg0CoDb/Ry23SZLlwLPPuv9OYVQ7in0dZfIyJHyrhXBefJNaa5VXv7d4kWMXncJqFEDyM+XjWXL4s/bHsLjvw7HusPN3D6XEMGrTO1tR3SXLr4FH6Eo3aDFLpRI2D0YDlXRtcDgg8jT2xZntr8kTz0l63/4yvbX3Nfqf6+8AjRtGl7JH/zgLvAQG7sAr77paGjYEHjqKXwZPwgpgyr69NKpnVvClx3RKSn6KfgYDjWs9Ih5TQITpXUHiDTlpcqqCyH8CzwAx19zW4ItwDGKYeM8qpGaKrNwrl8vx+TXr5dTLZEYeLwKmRgjKgq46y7gm2+APXtgGTEST73sPfAoSq139d7e7QLy3S7g+0sebLbY17kfRfuTnMzK1/5iXpPAMPigyKZ2lVVnzn/NbQm2atRQHpeU5DoxbBuzHjBA/hvGY7aXrl3yHHhUqgS89BLw55/AypUyOVhUVEAvnVrv6v15t+vPSx5M/sS+5DuOKAVG9eCjdu3aMJlMLh/Dhw9X+1REJRfo25HKlT2/hbQxmdz/NY/QUQ1PWs1tiXJvlFO0PbYdEKtuBRYtknf5yZOBWrUUx/jz0qn9rt7fd7t6ecn1EgiFE44oBUb1NR9bt26FxWKxP969eze6deuGe++9V+1TEZVcIG9HkpOBf/9bFiFzXhRqU6kSMH++57/mkbASz5usLJg+quvSbM16GKax3ou7+fvSqfmuPpB3u3p5ycMtnbvWvJUrAjii5E7Qt9qOGjUKX331Ffbv3w+Tt3eK4FZb+luo9uh5K8HpzvLlju22zotC4+Nl2yuv8K+NO1YrsHYtMHs2TLeucvm0GH5ajir5wNfs8MFIY+7t3BG0I5r+xqro/t2/gxp8XL16FYmJiRg9ejRefvllt8cUFBSgoKDA/jgvLw/JyckMPiJZKCq6Op/Pl90u7kYzIiWRQUmdOwcsWADMmQPLwQMoNcH1kGIr0nrgqSS9TVpa8OJAT+eOtNwO5BDpfw50U9vlP//5jzCbzSInJ8fjMRMmTBAAXD6Y4TRChSr/s7vzekr/WKmSTKHuJvVjpKdT9mrnTiGGDhWiTBkhAPHQ3a7ZSu/89M4SnULLTJqRlsWTqDi6yXDao0cPREdH48svv/R4DEc+IsTVq8oy6U89BURHK4/xNgUS7LFs29uWnBy5nbZKFbkyz8Pbl1AP0BjG1atyamrWLGDTJnuz6VXXQwvGFiDaHO36CT9p+Y4z0t/tEtnoYtrl8OHDqFu3LjIyMpCSkuLz13HNRxh64QVg6lT5V9rGbAZGjwbeesvRZqBa3Eyn7MbRo8C8ecB77wEnT8q2UqWAe+6B6Yb/uBweyDQLEemXP/fvoOX5WLBgAapWrYrevXsH6xRkBC+8ALz9tjLwAOTjt9+Wn7cxSLYeXxNMOV9yWBJCBoP9+slRq4kTZeCRkCAXXBw5wsCDiFwEJfiwWq1YsGABBg0ahFKlmME9Yl29Kkc8ijN1qjwOMEy2Hn8STIWt/Hw5jda0KXDHHXKaxWIBOnaUtW8OH8bETlEwzVcWhqtRvgYDDyIKTm2X7777DkeOHMGjjz4ajKcnrfk6yf3uu97f/lss8rhRo3yvfaJxth6DDNAEx5498vVatMhR3K1cOeChh4Dhw2UwAvdp0s+/eB5xsXGh7C0R6VRQgo/u3bsjiOtYSSsWCzBpklxRedZDqfmiDh707XltxxkkW49BBmjUU1gIfPmlLGG/bp2jvWFDGXAMGgTEOYIKt2nSOdpBREWwtgv5JiMDqFYNmDBBGXgAjjKeGRnK9nr1fHvuoscZIP9zxKRTPnUKeOMNoG5d+X23FXdLSbEXd8MzzzDwICK/BT3Dqb+420WHfEnC5W4b7NWrQNmyxU+9mM3ApUvut93qeP9i2CaYEgL4+Wc5yvHZZ471OJUrA0OHAk884VJjBQDSd6VjYMZA16dj4EEUMfy5f3M1KBXP15LzRVdZ2rbBRkfL7bRvv+3560aPdg08AP0UwvDANkDjLs+HIdMpX74MpKfLoGPHDkd769bAiBHAvfcCsbFuv9TdaMehkYdQq6JrkEJEBDD4IKD4UQZ/65Y7r7K05fHwJc+HwYRFga4//wTmzAE+/NAxnRYTA/TvL9dz3HprsV/OaRYiCgSDj0jnLU2nv1s23K2yfOstmf/BW4ZTA9L5AI17tuJus2YBq1c7RrVq1waefBJ49FGfirsx8CCiQHHNRyTzJU1nfLxvWUcBucqSZTz16+xZe3E3xU6kHj3kKMedd/r02m08vBEdF3Z0aWfgQRTZuOaDvPOWptNkkrk3DhwoPvdGUTrYBktu7Ngh13Kkp8u1HYDcofLoo3Kko0EDn5/K3WjH1se24pbEW9TqLRFFAAYfkcrXNJ2bNnnOvWHjrtQ8FSvom3kKCuTI1ezZwObNjvabbpKjHAMHyuRgfuA0CxGphcFHpPInTeeAAe63dlSqJPM8vPIKRzz8ENRquNnZjuJup07JtlKlZPA4fDjQvr3bBCXegiEGHkSkJgYf4ciXt9X+pukMi60d2vO0zMaWpy2g/CBCyIrAs2YBK1c6dhUlJgLDhgGPPVbs611cMNSk4140nt3Y9ZQMPIioBLjgNNz4+rbaYpG7G7zVUeECUtXYvuWeZrv8/pbn5wMffSR3Ef3vf47222+XuTlSUoDSpYt9iuLWHIsJrqMdX/T/An0a9fGhc0QUabjgNFL587baIHVUwok/1XCL3b67Z49cy/HRR8ribg8/LLcw/13czZti1xy7CTw42kFEamFtl3DhbfcKIHevFE30ZYA6KuGkRNVwCwtlcNmlC3DjjTL4yM8HGjUCZs6UAaatxL2PPAZDrzLwIKLg4shHuAj0bTXXcoRMQNVwT56Ui0fnzXO8vlFRwF13yQWkXbp4rnDnhUuQU+4U8Hw1l+MWN2TgQUTqYvARLkryttqQaTqNx1YN19symw63CWDTZkdxt2vX5AGVK8vFo088AdSsWeL+KIKccdGA+ZrygK//D/hlBBLWl/hUREQKDD6MwtsOloDeVlMoeVtmU0Zcwop/psPcejawc6fjk23aOIq7xcSo1h9bMHR0qJuRk1eFDIaS5XFERGrimg8jWLZMBg2dO8vkUJ07y20TGRmOY2x3Ek9D8CaTTH/OO4mm3C2zqYuDmHPdv3DuuiS0mjNUBh6xscAjjwDbtskS9w8+qGrgAchgqLjAA+CaYyIKDgYfevfCC/Id7+nTyvajR+VbaFsAYntbDbgGILyT6EpqKnDoTyv++8Yq5Nx0Jw6YGmBY/r8RfeEcUKeOLMR39Kisw9KqVVD6cPnaZbeJw/CqHI7hmmMiCibm+dCzzz4D7ruv+GOci7m5y/ORnCwDD95JtHf2rCxfP2eOLGdv07OnnFrp2TPoAeI/3v8HtuRsUbQ9feszSC07wz6r166dzKzPNchE5Cvm+QgHFovM2eCN8w4W7l7RJ1txt8WLgStXZFvFio7ibvXrh6Qb7kY7rOOtMBUZLcvIAOrVC1L6dyIiMPjQr40bgb/+8u1Y5x0s3L2iD56Ku7Vo4SjuVrZsyLrjS32WoKR/JyJywuBDr3zdOgtwB4veuCvuVrq0o7hbu3YB5+YIhFVYYX7NdeTLOfDwlqfOZJJ56lJSOJBGRCXDBad65WtAUaUKd7DogRDA998D99wjF41OmiQDjxo1gNdeA44ckVMuHqrKBsvQL4a6BB5d6nRxm7HUnzx1REQlwZEPvbInYSjmbgDIlNp8G6qdvDxHcbc9exztnTrJBaR33eW1uFuwuJtmufLKFcSUcr9lt0Tp34mI/MDgQ6+KZqTytCHp+efl5yn0/vc/R3G3CxdkWwDF3YLFl/Udzpinzhi85RskMgIGH3pmy0jlvHW2ShV547v3Xu36FokKC4GVK+X3fn2RnOONG8u1HA8/DOhge3gggQfgR/p3zvJpxt1Oeu5EIiNing8j4FsdbdmKu82dK+/MgCzulpIig4477oDFatL8JXrzxzfx0rqXFG1VylbBqedP+fwctt0ugGv6d4C7XbTkaScSXxvSC3/u3ww+iNwRQmbZmj1b/lW3FXerUkUWdxs2zF7cTQ/vRt2Ndpx94SyuL3O938/FPHX6Y7HIigqeloDZRqWK5hskCjUGH0SBunRJ7kqZPRv49VdH+z/+IReQ9uunqLGih3ejgU6zFIeDbfqSmSlLOnmzfj1T/JB2mOGUyF8HDsgdKwsWAOfPy7bYWJkIbPhw4OabXb5ED3kxghF4AMxTpzfciUThhsEHRS6LBVi9Wo5yrFnjaK9bV6Y8HzwYqFTJ45f7kxdD7Rv5F3u/QMqSFNdzqhB4kP5wJxKFGwYfFHnOnHEUd8vKkm0mk7K4W5T3/HtavRt1N9pxeNRh1Iyrqe6JSDe4E8mBU4LhgcEHRY7t2+UoR3q6srjbkCFypKNePb+eTot3o8GaZiF9K5r2x2RyvxNp+vTwvwnrYXE3qYMLToOJIbr2CgqAzz6TQcfPPzvaW7SQoxwDBgRc3M22A8Hbu1G1diAw8KBI3omkh8XdVDzudtEDhujaOnLEUdzt9GnZVrq0TMw2fDjQtq0qNVZCkRdja85WtH6/tUs7A4/IFInvabjV2BgYfGhN5RA9Ev/YBMRW3G3WLOCLLwCrVbbXqCHzcjz+OFCtmuqnDea7UXejHdsf346bE1x33xCFK241NgZutdWSyvsvOYDig7w8YNEiuVX2jz8c7Z07y1GOlBSgVPB+1FNT5SnUDhA5zUIkcatx+GHwoTYV9196GkDJyZHtET/H+fvvjuJuFy/KtuuucxR3a9IkZF1ROy8GAw8yglCNynKrcfhh8KE2lUJ0PSSw0qVr12Rxt1mzgA0bHO033CBHOR56SBfF3QJ16Pwh1JlRx6WdgQfpTShHZbnVOPx4T2YQgJycHDz44IOoVKkSypQpg2bNmmHbtm3BOJX+qBSi+zOAEhFOnABee02uOrv3Xhl4mM3yr9y6dXIUZPhwQwcepjSTS+CxauAqBh6kO7ZRWee/UbZR2YwMdc9n22oMuK4Tj6StxuFE9ZGPc+fOoX379ujcuTNWr16NKlWqYP/+/bj+ev8LXBmSSiE65zghv38//SSnVpYvdxR3q1pVLh4dNkx+L8MAp1nIKLQalU1NlVPN7kZbImGrcbhRPfh48803kZycjAULFtjb6tRxHUYOWyplA4roOc6LFx3F3f77X0d7u3ZydOOeexTF3YyOgQcZiZZlBYK1uJtCT/Vply+++AK33HIL7r33XlStWhUtW7bEe++95/H4goIC5OXlKT4Mzxai16ihbE9K8nmVqG0AxVMqCpNJbucMqznOAweA0aPl9+3xx2XgUaaMzEC6Y4ccBRk4MGwCj/NXzjPwIMPRelTWtrh7wAD5LwMPY1I9+Pjzzz8xZ84cNGjQAGvXrsWTTz6JZ555BosWLXJ7/OTJkxEXF2f/SE5OVrtL2khNBQ4dkhvPFy+W/2Zl+Tw2GDFznBYL8NVXsp5KgwbAtGlAbq4s7vbOO/It1vvvAy1bat1TVbX/sD2uf1M5FfnJ3Z8w8CDdi+hRWVKN6knGoqOjccstt2DTpk32tmeeeQZbt27F5s2bXY4vKChAQUGB/XFeXh6Sk5ONnWRMRWGbTtlW3O3dd2WQBsioqlcvmfa8Rw+firsZEUc7yMhCXVaAjEPTJGMJCQm48cYbFW033HADli9f7vb4mJgYxITJMHowhN0cp7vibtdfDzz6aEDF3YyGgQcZHYvckRpUDz7at2+PvXv3Ktr27duHWrVqqX2qiKF2AquQKygAli6VQceWLY72li3lKEf//gEXdzOKgsICxE6KdWln4EFGxJ0nVFKqBx/PPvss2rVrhzfeeAP33XcffvnlF8yfPx/z589X+1Skd0eOAHPnyjUbQSzupnePrnwUC35doGh74443MKbDGI16RFRyYTcqSyEVlMJyX331FcaMGYP9+/ejTp06GD16NB577DGfvjYsCstFMiFk0q/Zs5XF3ZKSgCeeAIYODUpxN71yN81iHW+FKQKCLiKKLKxqS6GXm+so7lZ02u2OO+Qox113BbW4mx5xfQcRRRJWtaXQ2b1bjnJ8/LGyuNugQbK429+Ljy0WYGNmZAzPCiEQ9ZrrTh0GHkREEoMP8t+1a8Dnn8vibj/84Gi/8UZHcbfy5e3NGRnAM8/IrXk2NWoAM2d6XpgWqmqZapv0wySMXT9W0Ta05VC8d5fnRHtERJGGwQf57vhx4L33gHnzgGPHZJvZDPTtK4OOTp1cFpBmZMhs6M5ycmT78uWuAUgoq2Wqyd00S8HYAkSbozXoDRGRfnHNBxXPVtxt1iwZKRQWyvZq1YDHHiu2uJvFIg87c8bz01eqBJw86RjVsFXLdP6ptMU0PmanDzmu7yCiSMc1H8Fi1LmAQFy8CHz6qVzP8dtvjvZ27WRujnvuAaKLf0efmVl84AHIz2dmAl26aFcts6QYeBAR+Sc881cHQ0aGzCncubMsbta5s3yckaF1z9S1fz/w7LNyUcawYTLwKFNGbpHduVOOggwY4DXwAGRQ4Qvbcf5UywwGi0X2JT1d/muxFH/82gNrXQKP22rexsCDiMgLjnz4wtNcQE6ObNfrXICvLBbg66/lKMfatY72evXkjpXBg2UK9CDTslqmv+tM3I12nH/xPOJi49TvHBFRmOHIhzfe5gIAORfg7W2yHv31F/Dmm0D9+jIPx9q1cm6jd28ZjOzbJ0vcBxh4+JoS3nacVtUybbGl86iLLbZ0HtzyNM3CwIOIyDdccOpNZqacYvFm/XrjFGDZulWOcixZIuuuADLAGDJEFnerW1eV0/i74FSLapm2c3qa7nE+J9d3EBG558/9myMf3mg5F6CmK1eAjz4C2rQBWreW2UgLCoCbb5al7XNygLffVi3wAOTN2ltJn/nzHYGErVom4FryJVjVMn1dZ/LRmt9dAo87G9zJwIOIKAAMPrzRai5ALYcPA2PGAMnJMuvoL7/IxaIPPghs3gxs2ybXdJQpE5TTp6bKHbrOu3GTktzn+LBVy6xRw/X4YCyt8SlmHNEYj25rqmg6+a+TWDVwlbqdISKKEJx28UaLuYCSslqB776TUytffeUo7pac7CjuVrVqSLvk7y7lUO1q9jqr9iqnWYiIfME8H2qyzQX06ycDjaIBSLDmAgJ1/ryjuNu+fY72Ll1kBtI+fTQr7mY2+7ckxt/jA9Whg4wd3caWDDyIiIKC0y6+CPVcgL9++03m5KhRQ+682bdP1lZ5+mlgzx45CnL33RFXVdYXbteZVMh2CTy61+vOwIOISCWcdvGHnjKcXrsGrFgh054XzbrVpIkc5XjwQUVxNyqePc/H7b2ABmsUnzs86jBqxtXUqGdERMbAaZdgCdVcQHGOHZNbRObPd6yWNJvlyMaIEUDHjq5bRcir1FTgnl2cZiEiCgUGH0YgBPDjj3KUIyNDWdxt2DDg8cddp4TIL8zfQUQUOgw+9MxW3G3WLGDXLkf7bbfJqZXUVJ9qrJBnZy+fRaW3KinaGsQ3wL6n93n4CiIiKikGH3q0b5/csbJwIZCbK9vKlgUeeEAGHTfdpGn3wsWTXz2JudvnKtp+e+I3NKvWDIC+lvgQEYUTBh96YbEAq1bJ3BzffONor19fBhyPPAJUrKhV78KOt2kWfwvNERGR7xh8aO2vv4APPgDmzJHZSAG5YPSf/5RBR7duQBR3RKvJl8AjnIsYExFpjVtttbJ1q1zL8Z//OIq7xcfL7KNPPAHUqaNt/8LQ5WuXUfaNsoo2E0ywTrDaH/tbaI6IiCQWltOrK1dkBtLWreXHRx/JwKNVK2DBAnnHe/NNBh5BMOmHSS6Bx4ZHNigCD8D3QnNFU6sQEZF/OO0SCocOAXPnAu+/76gvHx0N3HefzM3RujVzcwSRP9tow6WIMRGRnjH4CBZbcbdZs2RxN9vsVs2aclplyJCQF3eLRP7m7zB6EWMiIiNg8KG28+flFtl33wX273e0d+0qRzl692aNlRAotBai9OulXdq9JQ4rttAcHGs+OnRQq6dERJGHaz7UUrS427PPysCjQgXgmWeAP/4Avv0WSElh4BECH+z4wCXwWH7fcp8ylrotNAflY70UMSYiMireCUvi6lVZ3G32bOUKxKZNHcXdrrtOu/5FIHfTLNbxVpj8WFNjK2LsLs/H9OncZktEVFIMPgJhK+42bx5w4oRsM5vlXWnECDkmzwWkIadmfZbUVDlQxQynRETqY/DhKyHknWjWLDnaYSvuVr26o7hbYqK2fYxQQghEveY6g1jSwnB6KGJMRBSOGHx4c+EC8Mkncmpl925He4cOcmrl7rtZ3E1DX+79EnctuUvRNqf3HDxxyxMa9YiIiLxh8OHJ3r2O4m55ebKtbFm5jmP4cKB5c027pyW9FFxzN81ydexVlDa77nIhIiL9YPBRlMUic3LMmiVzdNg0aAA89RSLu0E/BdfUXN9BREShxeDD5upVoEkT4MAB+dhW3G3ECJmjg8XddFNwjYEHEZGx8Y5qEx0N3HILUKkS8MILwMGDwBdfAN27M/CAHBQaOdJ94i1b26hR8rhg2Zy92SXwGNdxHAMPIiKD4chHUdOmAXFxQJkyWvdEd/wpuBaMHSI3z7sZO0/sVLRdGHMB5aLLqX8yIiIKKgYfRVWvrnUPNOHLAlItC65xmoWIKLww+Ihwvi4g1argGgMPIqLww8UMEcy2gNR5OsW2gDQjw9FmK7jmKXGryQQkJ6tXcO3Pc3+6BB7jO45n4EFEFAZUDz5effVVmEwmxUfjxo3VPg2VkL8LSENZcO2epfeg3sx6irZzL55DWue0kj85ERFpLijTLk2aNMF3RfJklGIlV90JZAFpKAqucZqFiCj8BSUqKFWqFKpH6OJNowh0AWkwC64x8CAiigxBCT7279+PxMRExMbGom3btpg8eTJq1qzp9tiCggIUFBTYH+fZUplTUJVkAanaBdfOXj6LSm9VUrSldUrD+NvHq3cSIiLSDZMQ7mb9A7d69WpcuHABjRo1wvHjx5GWloacnBzs3r0b5cuXdzn+1VdfRVqa61x+bm4uKlSooGbXqAiLBahdWy4udfcTYDLJ6ZSsLMeoRjBqukzdPBXPffOcou3086dRuWzlkj0xERGFVF5eHuLi4ny6f6sefDg7f/48atWqhalTp2LIkCEun3c38pGcnMzgIwRsu10AZQBiW0BaNF16MGq6cJqFiCh8+BN8BH2rbcWKFdGwYUMcsNVMcRITE4MKFSooPvTKYgEyM4H0dPlvMFOJh4JtAWmNGsr2pCTXwMPXLbm+YuBBRBS5gh58XLhwAQcPHkSC2tmnQiwjQ05TdO4MDBwo/61dO7Abr56kpgKHDgHr1wOLF8t/s7IcgYfaNV0uXr3oEng8+49nGXgQEUUQ1Rec/utf/0KfPn1Qq1YtHDt2DBMmTIDZbMaAAQPUPlXI6KWaa7AUt4BUzZouK/asQOpS5Tfq8KjDqBnnfjEyERGFJ9WDj6NHj2LAgAE4c+YMqlSpgttuuw0///wzqlSpovapgsNpVaWlXQeMHGn2+M7fZJLv/FNS1Nluqjdq1XRp+m5T/H76d0UbRzuIiCKT6sHHkiVL1H7K0HGzqvJalSTcenoGjsL90Eawq7lqTY2aLlzfQURERbG2i42HVZUxp3OwDP1wN4pf3BGMaq56UJKaLharhes7iIjIBYMPoNhVlSbItukYhSh4XlVp8PW0HgVa02X3qd0o9bpyYO34c8cxtcfU4HSUiIgMg8EH4HVVZRQEaiIbHbDR5XNqV3PVI1+35Nq8su4VNJvTTNEmJghUv44p94mIKEjp1Q3HxzmTRCiPU7uaq575WtPFeZqlStkqOPX8qRD2lIiI9I7BB+DznMm1ygnAX47HalZzNYLituQKIRD1mnIgbWbPmXi6zdPB7xgRERkKgw/AsarSS6GTJQc6YOMm9au5Gt3BswdR///qK9oOjTyEWhVradQjIiLSMwYfgGNVZb9+MtBwV+hk+nSYo81huZ22JN7+6W288N0LijbreCtMnrbHEBFRxGPwYWNbVemuelokza34IXZiLAosBYo2bqMlIiJvGHwU5euqygjnbn3H651fx9iOYzXqERERGQmDD2fFraok5OTlIGlakqLtj+F/oFHlRhr1iIiIjIZ5Pshn87bNcwk8LOMtDDyIiMgvHPkgn9ScVhPZedmKNq7vICKiQHDkg7wypZkUgcdzbZ9j4EFERAHjyAd5dPriaVR9p6qibeewnWhRvYU2HSIiorDA4IPcWrJ7CQYsH6Bouzr2KkqbS2vUIyIiChcMPsjFLfNvwfbj2xVtnGYhIiK1cM0HKZjSTIrA49EWjzLwICIiVXHkgwAAeQV5iJsSp2j7cfCPaF+zvUY9IiKicMXgg7Bq3yr8M/2firbLr1xGbKlYjXpEREThjMFHhOvxSQ98c/AbRRunWYiIKJgYfEQwU5qy8mzfxn2x4v4VGvWGiIgiBYOPCHTp2iWUe6Ocom3tg2vRvV53jXpERESRhMGHQVgs6hTbzTyUic6LOiva8sfk47ro61TqKRERUfEYfBhARgYwciRw9KijLSkJmDEDSE31/XkGLh+I9N3pijau7yAiolBj8KFzGRlAv36AcIoRcnJk+7JlvgUgzus7bqt5GzYO3qhiT4mIiHzDJGM6ZrHIEQ/nwANwtI0aJY/z5Krlqkvgsfy+5Qw8iIhIMxz50LGNG5VTLc6EALKz5XGdOrl+PicvB0nTkhRtZ144g/gy8ep2lIiIyA8c+dCx48cDP27VvlUugYeYIBh4EBGR5hh86FhCQmDHPbP6GUXG0n+1/RcXlhIRkW5w2kXHOnSQu1pyctyv+zCZ5Oc7dJCPrcKK+DfjkVuQaz9m57CdaFG9RWg6TERE5AOOfOiY2Sy30wIy0CjK9nj6dHncqYunYH7NrAg88sfkM/AgIiLdYfChc6mpcjttjRrK9qQkxzbb77O+R7V3qtk/17J6S4gJgonDiIhIlzjtYgCpqUBKivsMpy+vexmTf5xsP3ZKlyl48bYXNewtERFR8Rh8GITZrNxOK4RArem1cST3iL1t85DN+EfSP0LfOSIiIj8w+DCgc5fPIf4t5ZbZcy+eQ8XYitp0iIiIyA9c82Ewm7I3KQKPetfXg3W8lYEHEREZBoMPA5n0wyS0/7C9/fG4juNw4JkDMDlvhSEiItIxTrsYgBACzec2x+5Tu+1t6wetR6fanbTrFBERUYAYfOhcfkE+KkypoGg7/fxpVC5bWaMeERERlUzQp12mTJkCk8mEUaNGBftUYWf7se2KwKNK2Sqwjrcy8CAiIkMLavCxdetWzJs3D82bNw/macLS9J+n45b3brE/fvYfz+LU86e4voOIiAwvaMHHhQsX8MADD+C9997D9ddfH6zThKX2H7bHs2uftT9e/cBqTO0xVcMeERERqSdowcfw4cPRu3dvdO3atdjjCgoKkJeXp/iIVJeuXYIpzYRN2ZvsbcdGH0PP+j017BUREZG6grLgdMmSJdixYwe2bt3q9djJkycjLS0tGN0wlN2ndqPZnGb2xzHmGFx8+SLMUWYNe0VERKQ+1Uc+srOzMXLkSHz66aeIjY31evyYMWOQm5tr/8jOzla7S7o3f/t8ReDx2M2P4crYKww8iIgoLJmEEELNJ/z8889x9913w2x23DgtFgtMJhOioqJQUFCg+JyzvLw8xMXFITc3FxUqVPB4XLi489M7sfrAavvjFfevQN/GfbXrEBERUQD8uX+rPu3SpUsX7Nq1S9E2ePBgNG7cGC+++GKxgUckKSgsQOwk5cjQ4VGHUTOupkY9IiIiCg3Vg4/y5cujadOmirZy5cqhUqVKLu2Rav+Z/Wg4q6Gi7dq4aygVxZxvREQU/ljbJcQ+/e1TReDRv2l/iAmCgQcREUWMkNzxMjMzQ3Ea3bt/2f1Y+vtS++NPUz/FwGYDNewRERFR6PHtdghcs1xD9MRoRdv+p/ejfnx9jXpERESkHQYfQXb4/GHUnlFb0XbllSuIKRWjTYeIiIg0xjUfQZSxJ0MRePRu0BtigmDgQUREEY0jH0EyZOUQfPjrh/bH7/d5H0NuHqJhj4iIiPSBwYfKLFYLYibGwCIs9rbdT+5Gk6pNNOwVERGRfjD4UNGx/GOoMbWGou3iyxdRtnRZjXpERESkP1zzoZKv93+tCDw61uoIMUEw8CAiInLC4EMFo9aMQu/Fve2PZ/aciQ2PbNCwR0RERPrFaZcSsAorKr9VGeeunLO37Xh8B1omtNSwV0RERPrG4CNApy+eRtV3qira8l7KQ/mY8hr1iIiIyBg47RKA9VnrFYFHy+otISYIBh5EREQ+YPDhp7Hfj8UdH91hfzy5y2TsGLZDwx4REREZC6ddfCSEQJ0ZdXA497C9bdOjm9A2ua2GvSIiIjIeBh8+OHf5HOLfile0nX3hLK4vc71GPSIiIjIuTrt4sTl7syLwqFOxDqzjrQw8iIiIAsTgoxiTN05Guw/b2R+P7TAWf478EyaTScNeERERGRunXdwQQqDlvJb478n/2tu+f/h7dK7TWcNeERERhQcGH07yC/JRYUoFRdupf51ClXJVNOoRERFReOG0SxE7ju9QBB6VylSCZbyFgQcREZGKGHz8beaWmWg1v5X98cg2I/HXC38hysRvERERkZo47QKg44KO2Hhko/3x1wO/Rq8GvTTsERERUfiK6ODj0rVLKPdGOUVbzugcJJZPVOX5LRZg40bg+HEgIQHo0AEwm1V5aiIiIsOK2ODj91O/o+mcpvbHpaNK4/Irl2GOUic6yMgARo4Ejh51tCUlATNmAKmpqpyCiIjIkCJyQcMHOz5QBB5DWg7B1XFXVQ08+vVTBh4AkJMj2zMyVDkNERGRIZmEEELrThSVl5eHuLg45ObmokKFCt6/wE+9F/fG1/u/tj9eft9ypN6g3lCExQLUru0aeNiYTHIEJCuLUzBERBQ+/Ll/R9S0iylNmZn00MhDqFWxlqrn2LjRc+ABAEIA2dnyuE6dVD01ERGRIURM8JGdm614fHXsVZQ2l1b9PMePq3scERFRuImY4CM5Lhn9buyHGyrfgNc6vxa08yQkqHscERFRuIm4NR/BZlvzkZMjp1iccc0HERGFI3/u3xG52yWYzGa5nRaQgUZRtsfTpzPwICKiyMXgIwhSU4Fly4AaNZTtSUmynXk+iIgokkXMmo9QS00FUlKY4ZSIiMgZg48gMpu5nZaIiMgZp12IiIgopBh8EBERUUgx+CAiIqKQYvBBREREIcXgg4iIiEKKwQcRERGFlOrBx5w5c9C8eXNUqFABFSpUQNu2bbF69Wq1T0NEREQGpXrwkZSUhClTpmD79u3Ytm0b7rjjDqSkpOD3339X+1RERERkQCEpLBcfH4+3334bQ4YM8Xqs0QvLERERRSJ/7t9BzXBqsVjw2Wef4eLFi2jbtq3bYwoKClBQUGB/nJubC0BeBBERERmD7b7t05iGCILffvtNlCtXTpjNZhEXFydWrVrl8dgJEyYIAPzgBz/4wQ9+8CMMPrKzs73GCUGZdrl69SqOHDmC3NxcLFu2DO+//z42bNiAG2+80eVY55EPq9WKs2fPolKlSjA516QPsry8PCQnJyM7Ozssp3zC+fp4bcYVztfHazOmcL42IHjXJ4RAfn4+EhMTERVV/JLSoEy7REdHo379+gCAVq1aYevWrZgxYwbmzZvncmxMTAxiYmIUbRUrVgxGt3xm26kTrsL5+nhtxhXO18drM6ZwvjYgONcXFxfn03EhyfNhtVoVoxtEREQUuVQf+RgzZgx69eqFmjVrIj8/H4sXL0ZmZibWrl2r9qmIiIjIgFQPPk6dOoWHH34Yx48fR1xcHJo3b461a9eiW7duap9KdTExMZgwYYLLNFC4COfr47UZVzhfH6/NmML52gB9XF9I8nwQERER2bC2CxEREYUUgw8iIiIKKQYfREREFFIMPoiIiCikwj74mD17NmrXro3Y2Fi0adMGv/zyS7HHf/bZZ2jcuDFiY2PRrFkzfP3114rPCyEwfvx4JCQkoEyZMujatSv2798fzEvwyJ9re++999ChQwdcf/31uP7669G1a1eX4x955BGYTCbFR8+ePYN9GW75c20LFy506XdsbKziGD29boB/19epUyeX6zOZTOjdu7f9GL28dj/88AP69OmDxMREmEwmfP75516/JjMzEzfffDNiYmJQv359LFy40OUYf3+Pg8Hfa8vIyEC3bt1QpUoVVKhQAW3btnVJOfDqq6+6vG6NGzcO4lW45++1ZWZmuv2ZPHHihOI4PbxugP/X5+73yWQyoUmTJvZj9PDaTZ48GbfeeivKly+PqlWrom/fvti7d6/Xr9PDfS6sg4///Oc/GD16NCZMmIAdO3bgpptuQo8ePXDq1Cm3x2/atAkDBgzAkCFDsHPnTvTt2xd9+/bF7t277ce89dZbmDlzJubOnYstW7agXLly6NGjB65cuRKqywLg/7VlZmZiwIABWL9+PTZv3ozk5GR0794dOTk5iuN69uyJ48eP2z/S09NDcTkK/l4bIDP1Fe334cOHFZ/Xy+sG+H99GRkZimvbvXs3zGYz7r33XsVxenjtLl68iJtuugmzZ8/26fisrCz07t0bnTt3xq+//opRo0Zh6NChipt0ID8PweDvtf3www/o1q0bvv76a2zfvh2dO3dGnz59sHPnTsVxTZo0UbxuP/74YzC6Xyx/r81m7969ir5XrVrV/jm9vG6A/9c3Y8YMxXVlZ2cjPj7e5XdO69duw4YNGD58OH7++Wd8++23uHbtGrp3746LFy96/Brd3OfUKCSnV61btxbDhw+3P7ZYLCIxMVFMnjzZ7fH33Xef6N27t6KtTZs2YtiwYUIIIaxWq6hevbp4++237Z8/f/68iImJEenp6UG4As/8vTZnhYWFonz58mLRokX2tkGDBomUlBS1u+o3f69twYIFIi4uzuPz6el1E6Lkr920adNE+fLlxYULF+xtenntigIgVqxYUewxL7zwgmjSpImi7f777xc9evSwPy7p9ysYfLk2d2688UaRlpZmfzxhwgRx0003qdcxFfhybevXrxcAxLlz5zweo8fXTYjAXrsVK1YIk8kkDh06ZG/T42t36tQpAUBs2LDB4zF6uc+F7cjH1atXsX37dnTt2tXeFhUVha5du2Lz5s1uv2bz5s2K4wGgR48e9uOzsrJw4sQJxTFxcXFo06aNx+cMhkCuzdmlS5dw7do1xMfHK9ozMzNRtWpVNGrUCE8++STOnDmjat+9CfTaLly4gFq1aiE5ORkpKSn4/fff7Z/Ty+sGqPPaffDBB+jfvz/KlSunaNf6tQuEt985Nb5femG1WpGfn+/yO7d//34kJiaibt26eOCBB3DkyBGNeui/Fi1aICEhAd26dcNPP/1kbw+n1w2Qv3Ndu3ZFrVq1FO16e+1yc3MBwOVnrCi93OfCNvj466+/YLFYUK1aNUV7tWrVXOYlbU6cOFHs8bZ//XnOYAjk2py9+OKLSExMVPyA9ezZEx999BHWrVuHN998Exs2bECvXr1gsVhU7X9xArm2Ro0a4cMPP8TKlSvxySefwGq1ol27djh69CgA/bxuQMlfu19++QW7d+/G0KFDFe16eO0C4el3Li8vD5cvX1blZ10v3nnnHVy4cAH33Xefva1NmzZYuHAh1qxZgzlz5iArKwsdOnRAfn6+hj31LiEhAXPnzsXy5cuxfPlyJCcno1OnTtixYwcAdf5G6cWxY8ewevVql985vb12VqsVo0aNQvv27dG0aVOPx+nlPheUqrakb1OmTMGSJUuQmZmpWJjZv39/+/+bNWuG5s2bo169esjMzESXLl206KpP2rZti7Zt29oft2vXDjfccAPmzZuH119/XcOeqe+DDz5As2bN0Lp1a0W7UV+7SLF48WKkpaVh5cqVinURvXr1sv+/efPmaNOmDWrVqoWlS5diyJAhWnTVJ40aNUKjRo3sj9u1a4eDBw9i2rRp+PjjjzXsmfoWLVqEihUrom/fvop2vb12w4cPx+7duzVZMxSIsB35qFy5MsxmM06ePKloP3nyJKpXr+72a6pXr17s8bZ//XnOYAjk2mzeeecdTJkyBd988w2aN29e7LF169ZF5cqVceDAgRL32VcluTab0qVLo2XLlvZ+6+V1A0p2fRcvXsSSJUt8+sOmxWsXCE+/cxUqVECZMmVU+XnQ2pIlSzB06FAsXbrUZbjbWcWKFdGwYUPdv27utG7d2t7vcHjdALnr48MPP8RDDz2E6OjoYo/V8rUbMWIEvvrqK6xfvx5JSUnFHquX+1zYBh/R0dFo1aoV1q1bZ2+zWq1Yt26d4l1yUW3btlUcDwDffvut/fg6deqgevXqimPy8vKwZcsWj88ZDIFcGyBXML/++utYs2YNbrnlFq/nOXr0KM6cOYOEhARV+u2LQK+tKIvFgl27dtn7rZfXDSjZ9X322WcoKCjAgw8+6PU8Wrx2gfD2O6fGz4OW0tPTMXjwYKSnpyu2Rnty4cIFHDx4UPevmzu//vqrvd9Gf91sNmzYgAMHDvgU8Gvx2gkhMGLECKxYsQLff/896tSp4/VrdHOfU23pqg4tWbJExMTEiIULF4r//e9/4vHHHxcVK1YUJ06cEEII8dBDD4mXXnrJfvxPP/0kSpUqJd555x2xZ88eMWHCBFG6dGmxa9cu+zFTpkwRFStWFCtXrhS//fabSElJEXXq1BGXL1/W9bVNmTJFREdHi2XLlonjx4/bP/Lz84UQQuTn54t//etfYvPmzSIrK0t899134uabbxYNGjQQV65c0fW1paWlibVr14qDBw+K7du3i/79+4vY2Fjx+++/K65fD69bINdnc9ttt4n777/fpV1Pr11+fr7YuXOn2LlzpwAgpk6dKnbu3CkOHz4shBDipZdeEg899JD9+D///FOULVtWPP/882LPnj1i9uzZwmw2izVr1tiP8fb90uu1ffrpp6JUqVJi9uzZit+58+fP24957rnnRGZmpsjKyhI//fST6Nq1q6hcubI4deqUrq9t2rRp4vPPPxf79+8Xu3btEiNHjhRRUVHiu+++sx+jl9ctkOuzefDBB0WbNm3cPqceXrsnn3xSxMXFiczMTMXP2KVLl+zH6PU+F9bBhxBC/N///Z+oWbOmiI6OFq1btxY///yz/XO33367GDRokOL4pUuXioYNG4ro6GjRpEkTsWrVKsXnrVarGDdunKhWrZqIiYkRXbp0EXv37g3Fpbjw59pq1aolALh8TJgwQQghxKVLl0T37t1FlSpVROnSpUWtWrXEY489pskfCiH8u7ZRo0bZj61WrZq48847xY4dOxTPp6fXTQj/fy7/+OMPAUB88803Ls+lp9fOtgXT+cN2PYMGDRK33367y9e0aNFCREdHi7p164oFCxa4PG9x369Q8ffabr/99mKPF0JuK05ISBDR0dGiRo0a4v777xcHDhwI7YUJ/6/tzTffFPXq1ROxsbEiPj5edOrUSXz//fcuz6uH102IwH4uz58/L8qUKSPmz5/v9jn18Nq5uyYAit8hvd7nTH9fABEREVFIhO2aDyIiItInBh9EREQUUgw+iIiIKKQYfBAREVFIMfggIiKikGLwQURERCHF4IOIiIhCisEHERERhRSDDyIiIgopBh9EREQUUgw+iIiIKKQYfBAREVFI/T+f7togvcIURAAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x_train=x.reshape(-1,1)\n",
+    "y_train=y.reshape(-1,1)\n",
+    "from sklearn.linear_model import LinearRegression as LR\n",
+    "regressor=LR()\n",
+    "regressor.fit(x_train,y_train)\n",
+    "inct=regressor.intercept_\n",
+    "coff=regressor.coef_\n",
+    "X=np.linspace(0,2,50).reshape(-1,1)\n",
+    "Y=coff*X+inct\n",
+    "plt.plot(x,y,\"bo\")\n",
+    "plt.plot(X,Y,\"r\")\n",
+    "plt.plot(x_out,y,\"g\")\n",
+    "plt.plot(x,y_out,\"ro\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Q2.ipynb b/Q2.ipynb
new file mode 100644
index 0000000..8fee7e1
--- /dev/null
+++ b/Q2.ipynb
@@ -0,0 +1,454 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 136,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.linear_model import LinearRegression as LR\n",
+    "from sklearn.model_selection import train_test_split as tts"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "IMPORTING DATA"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 121,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df=pd.read_csv('Copy of AirQualityUCI.csv')\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "MISSING VALUE"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 122,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df1=df.replace(-200,0)\n",
+    "c=np.array(df.columns)\n",
+    "c1=np.array(df1.columns)\n",
+    "for i in range(2,len(c)):\n",
+    "    df[c[i]]=df[c[i]].replace(-200,df1[c[i]].mean())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "CHECKING CORRELATION"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 123,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 123,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import seaborn as sns\n",
+    "dff=df.iloc[:,2:]\n",
+    "corr = dff.corr(method = 'pearson')\n",
+    "\n",
+    "sns.heatmap(corr,annot=True,fmt=\".2f\", linewidth=.5)\n",
+    "#plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "OUTLIERS"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 124,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#I have deleted outliers of NOx as it has highest correlation with RH\n",
+    "\n",
+    "Q1=df[\"NOx(GT)\"].quantile(0.25)\n",
+    "Q2=df[\"NOx(GT)\"].quantile(0.75)\n",
+    "upper=Q2+1.5*(Q2-Q1)\n",
+    "lower=Q2-1.5*(Q2-Q1)\n",
+    "df3=df[(df[\"NOx(GT)\"]>lower)&(df[\"NOx(GT)\"]<upper)]\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "DROPPING UNNECESSARY DATA"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 125,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 125,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df3=df3.drop(\"Date\",axis=1)\n",
+    "df3=df3.drop(\"Time\",axis=1)\n",
+    "df3=df3.drop(\"PT08.S1(CO)\",axis=1)\n",
+    "df3=df3.drop(\"PT08.S2(NMHC)\",axis=1)\n",
+    "df3=df3.drop(\"PT08.S3(NOx)\",axis=1)\n",
+    "df3=df3.drop(\"PT08.S4(NO2)\",axis=1)\n",
+    "df3=df3.drop(\"PT08.S5(O3)\",axis=1)\n",
+    "corr = df3.corr(method = 'pearson')\n",
+    "\n",
+    "sns.heatmap(corr,annot=True,fmt=\".2f\", linewidth=.5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "SPLITING DATA"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 321,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X1=df3['CO(GT)'].to_numpy().reshape(-1,1)\n",
+    "X2=df3['NMHC(GT)'].to_numpy().reshape(-1,1)\n",
+    "X3=df3['C6H6(GT)'].to_numpy().reshape(-1,1)\n",
+    "X4=df3['NOx(GT)'].to_numpy().reshape(-1,1)\n",
+    "X5=df3['NO2(GT)'].to_numpy().reshape(-1,1)\n",
+    "X6=df3['T'].to_numpy().reshape(-1,1)\n",
+    "ones=np.ones([8393,1]).reshape(-1,1)\n",
+    "\n",
+    "Y=df3['RH'].to_numpy().reshape(-1,1)\n",
+    "X=np.hstack([ones,X3,X4,X5,X6])\n",
+    "X1=np.hstack([X3,X4,X5,X6])\n",
+    "y_max=np.max(np.abs(Y))\n",
+    "x_max=np.max(np.abs(X))\n",
+    "x_1max=np.max(np.abs(X1))\n",
+    "\n",
+    "Y_=(Y-y_max)/y_max\n",
+    "X_=(X-x_max)/x_max\n",
+    "X_1=(X1-x_1max)/x_1max\n",
+    "\n",
+    "x_train1,x_test1,y_train1,y_test1=tts(X_1, Y_, test_size=0.15,  random_state =42)\n",
+    "\n",
+    "x_train,x_test,y_train,y_test=tts(X_, Y_, test_size=0.15,  random_state =42)\n",
+    "m=y_train.shape[0]\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "HYPOTHESIS as m+n*X3+o*X4+p*X5+q*X6\n",
+    "seeing very less correlation of CO and NMHC, not accounted them in hypothesis\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 297,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def hypo(theta,x_train):\n",
+    "    return x_train.dot(theta)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "COST FUNCTION"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 298,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def cost_function(y_train,y_out):\n",
+    "    \n",
+    "    l=(y_train-y_out)**2/2\n",
+    "    cf=l.mean()\n",
+    "    return cf\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "GRADIENT DESCENT"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 299,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def gd(x_train,y_train,m,theta,learning_rate):\n",
+    "    y_out=hypo(theta,x_train)\n",
+    "    dm=(1/m)*np.sum(np.multiply((y_train-y_out),-X_[0]))\n",
+    "    dn=(1/m)*np.sum(np.multiply((y_train-y_out),-X_[1]))\n",
+    "    do=(1/m)*np.sum(np.multiply((y_train-y_out),-X_[2]))\n",
+    "    dp=(1/m)*np.sum(np.multiply((y_train-y_out),-X_[3]))\n",
+    "    dq=(1/m)*np.sum(np.multiply((y_train-y_out),-X_[4]))\n",
+    "\n",
+    "    theta[0]=theta[0]-learning_rate*dm\n",
+    "    theta[1]=theta[1]-learning_rate*dn\n",
+    "    theta[2]=theta[2]-learning_rate*do\n",
+    "    theta[3]=theta[3]-learning_rate*dp\n",
+    "    theta[4]=theta[4]-learning_rate*dq\n",
+    "\n",
+    "    return theta"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 212,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1. , 0.5, 0. ])"
+      ]
+     },
+     "execution_count": 212,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x=np.array([9,8,7])\n",
+    "mx=np.max(x)\n",
+    "mn=np.min(x)\n",
+    "x=(x-mn)/(mx-mn)\n",
+    "x"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "IMPLEMENT USING GD"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 300,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "theta=np.empty((5,1))\n",
+    "\n",
+    "learning_rate=0.005\n",
+    "noche=10000\n",
+    "\n",
+    "L=[]\n",
+    "\n",
+    "for i in range(noche):\n",
+    "    theta=gd(x_train,y_train,m,theta,learning_rate)\n",
+    "    y_out=hypo(theta,x_train).reshape(-1,1)\n",
+    "    L.append(cost_function(y_train,y_out))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 303,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.0, 0.1)"
+      ]
+     },
+     "execution_count": 303,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(L,\"r\")\n",
+    "plt.ylim(0,.1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "USING SCIKIT"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 322,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "from sklearn.linear_model import LinearRegression as LR\n",
+    "regressor=LR()\n",
+    "regressor.fit(x_train1,y_train1)\n",
+    "\n",
+    "predictions=regressor.predict(x_test1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "MEAN SQUARED ERROR"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 323,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MSE of data by scikit : 0.02064605882647789\n",
+      "MSE of data by GD : 0.03946085388001057\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import mean_squared_error\n",
+    "print(\"MSE of data by scikit :\", mean_squared_error(y_test,predictions))\n",
+    "y_rs=x_test.dot(theta)\n",
+    "g=y_test.shape[0]\n",
+    "er=(1/g)*np.sum(np.multiply((y_test-y_rs),(y_test-y_rs)))\n",
+    "print(\"MSE of data by GD :\", er)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "CALCULATION OF R2 SCORE"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 333,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "R2 score of GD model: 0.9999796189689623\n",
+      "R2 score of SCIKIT model: 0.9999893365722133\n"
+     ]
+    }
+   ],
+   "source": [
+    "RSS1=mean_squared_error(y_test,predictions)*g\n",
+    "RSS=er*g\n",
+    "TSS=np.sum(np.multiply((Y-Y.mean()),(Y-Y.mean())))\n",
+    "R2=1-(RSS/TSS)\n",
+    "R2_1=1-(RSS1/TSS)\n",
+    "print(\"R2 score of GD model:\",R2)\n",
+    "print(\"R2 score of SCIKIT model:\",R2_1)\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Suyash_231067/.ipynb_checkpoints/Assignment1-checkpoint.ipynb b/Suyash_231067/.ipynb_checkpoints/Assignment1-checkpoint.ipynb
new file mode 100644
index 0000000..80aea74
--- /dev/null
+++ b/Suyash_231067/.ipynb_checkpoints/Assignment1-checkpoint.ipynb
@@ -0,0 +1,1015 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "UOzd0zRXW5Uv"
+   },
+   "source": [
+    "# Week-1 Assignment\n",
+    "\n",
+    "*Welcome to your first assignment for the SimuTech Winter Project 2022! I hope you are excited to implement and test everything you have learned up until now. There is an interesting set of questions for you to refine your acquired skills as you delve into hands-on coding and deepen your understanding of numpy, pandas, and data visualization libraries.*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "2XFUXyq9Y9TG"
+   },
+   "source": [
+    "# Section 0 : Importing Libraries"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "D_bzcLusjSO0"
+   },
+   "source": [
+    "*Let's begin by importing numpy, pandas and matplotlib.*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "id": "jdwX7bI7aGeY"
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib as mp"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "LM25mz5ta-Q_"
+   },
+   "source": [
+    "# Section 1 : Playing with Python and Numpy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "TcFQVqhAdQw_"
+   },
+   "source": [
+    "### Q1. Matrix Multiplication"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "6Dcai9pys0j8"
+   },
+   "source": [
+    "##### (i) Check if matrix multiplication is valid"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "id": "0qC4txIss3gG"
+   },
+   "outputs": [],
+   "source": [
+    "\n",
+    "def isValid(A,B):\n",
+    " c1=A.shape[1]\n",
+    " r2=B.shape[0]\n",
+    " if c1==r2:\n",
+    "  return True\n",
+    " else:\n",
+    "  return False\n",
+    "    \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "OPTfRGlTdXxa"
+   },
+   "source": [
+    "##### (ii) Using loops (without using numpy)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "id": "V8F5ETsRct3S"
+   },
+   "outputs": [],
+   "source": [
+    "def matrix_multiply(A,B):\n",
+    " r1,c1=A.shape\n",
+    " r2,c2=B.shape\n",
+    " z=np.zeros((r1,c2))\n",
+    " for a in range(len(A)):      \n",
+    "   for b in range(len(B[0])):      \n",
+    "          for c in range(len(B)):      \n",
+    "               z[a][b] += A[a][c] * B[c][b] \n",
+    " return(z)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "umUgUTSWdos8"
+   },
+   "source": [
+    "##### (iii) Using numpy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "id": "-tdXlCHrduBP"
+   },
+   "outputs": [],
+   "source": [
+    "def matrix_multiply_2(A,B):\n",
+    " return(np.dot(A,B))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "2nMFnl84edJG"
+   },
+   "source": [
+    "##### (iv) Testing your code\n",
+    "\n",
+    "Run the following cell to check if your functions are working properly.\n",
+    "\n",
+    "*Expected output:*\n",
+    "[ [102 108 114]\n",
+    " [246 261 276]\n",
+    " [390 414 438]\n",
+    " [534 567 600] ]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {
+    "id": "09rX_Cm9ezmq"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Result using loops: [[102. 108. 114.]\n",
+      " [246. 261. 276.]\n",
+      " [390. 414. 438.]\n",
+      " [534. 567. 600.]]\n",
+      "Result using numpy: [[102 108 114]\n",
+      " [246 261 276]\n",
+      " [390 414 438]\n",
+      " [534 567 600]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "A = np.array([\n",
+    "    [1, 2, 3],\n",
+    "    [4, 5, 6],\n",
+    "    [7, 8, 9],\n",
+    "    [10, 11, 12]\n",
+    "])\n",
+    "\n",
+    "B = np.array([\n",
+    "    [13, 14, 15],\n",
+    "    [16, 17, 18],\n",
+    "    [19, 20, 21]\n",
+    "])\n",
+    "\n",
+    "if isValid(A,B):\n",
+    "  print(f\"Result using loops: {matrix_multiply(A,B)}\")\n",
+    "  print(f\"Result using numpy: {matrix_multiply_2(A,B)}\")\n",
+    "else:\n",
+    "  print(f\"Matrix multiplication is not valid\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "5hfP7Ao1fwqV"
+   },
+   "source": [
+    "### Q2. Z-Score Normalisation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "0e81Xx5Kw_CQ"
+   },
+   "source": [
+    "Z-score normalization refers to the process of normalizing every value in a dataset such that the mean of all of the values is 0 and the standard deviation is 1.\n",
+    "\n",
+    "We use the following formula to perform a z-score normalization on every value in a dataset:\n",
+    "\n",
+    "New value = (x – μ) / σ\n",
+    "\n",
+    "where:\n",
+    "\n",
+    "x: Original value\n",
+    "\n",
+    "μ: Mean of data\n",
+    "\n",
+    "σ: Standard deviation of data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "GP_MuyUbguSR"
+   },
+   "source": [
+    "##### (i) Without using numpy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "id": "0vix8qaaghwP"
+   },
+   "outputs": [],
+   "source": [
+    "def mean(x):\n",
+    " i=0\n",
+    " b=0\n",
+    " for a in x:\n",
+    "    b=b+a\n",
+    "    i+=1\n",
+    " return(b/i)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "id": "j1qabel-g_f5"
+   },
+   "outputs": [],
+   "source": [
+    "import math\n",
+    "def standard_deviation(x):\n",
+    " i=0\n",
+    " s=0\n",
+    " m=mean(x)\n",
+    " for b in x:\n",
+    "  s+=(b-m)*(b-m)\n",
+    "  i+=1\n",
+    "    \n",
+    " u=math.sqrt(s/i)\n",
+    " return(u)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "id": "797ewbNqhEpY"
+   },
+   "outputs": [],
+   "source": [
+    "def zscore_normalisation(x):\n",
+    "  p=standard_deviation(x)\n",
+    "  q=mean(x)\n",
+    "  a=0\n",
+    "  i=0\n",
+    "  for a in x:\n",
+    "    x[i]=(a-q)/p\n",
+    "    i+=1\n",
+    "  return(x)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "k8dq1wqRhbUB"
+   },
+   "source": [
+    "##### (ii) Using numpy\n",
+    "\n",
+    "Numpy has in_built functions for calculating mean and standard deviation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "id": "WjxJTUDihsYg"
+   },
+   "outputs": [],
+   "source": [
+    "def zscore_normalisation_2(x):\n",
+    "  m=np.mean(x)\n",
+    "  s=np.std(x)\n",
+    "  b=(x-m)/s\n",
+    "  return(b)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "glRqKU-_1pue"
+   },
+   "source": [
+    "##### (iii) Testing your code"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Kl7XBymOigCU"
+   },
+   "source": [
+    "Run the following cell to check if your functions are working properly.\n",
+    "\n",
+    "*Expected Output:* [-1.06753267 -0.99745394 -0.99745394 -0.81057732 -0.41346451 -0.06307086\n",
+    "  0.31068237  0.91803138  1.22170588  1.89913361]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "id": "78ptZxf6ipZp"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Result without using numpy: [-1.0675326683028088, -0.9974539373420117, -0.9974539373420117, -0.8105773214465528, -0.41346451266870277, -0.06307085786471743, 0.3106823739262003, 0.9180313755864415, 1.2217058764165623, 1.8991336090376005]\n",
+      "Result using numpy: [-1.06753267 -0.99745394 -0.99745394 -0.81057732 -0.41346451 -0.06307086\n",
+      "  0.31068237  0.91803138  1.22170588  1.89913361]\n"
+     ]
+    }
+   ],
+   "source": [
+    "x = [4, 7, 7, 15, 32, 47, 63, 89, 102, 131]\n",
+    "print(f\"Result without using numpy: {zscore_normalisation(x)}\")\n",
+    "print(f\"Result using numpy: {zscore_normalisation_2(x)}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "0WVscJ0z14rp"
+   },
+   "source": [
+    "### Q3. Sigmoid fn and its derivative"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Jfntb0Rg94Tu"
+   },
+   "source": [
+    "The sigmoid function is a mathematical function that maps any input value to a value between 0 and 1.\n",
+    "\n",
+    "It is defined mathematically as s(x) = 1/(1+e^(-x))."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "8SCAPkjO2m7u"
+   },
+   "source": [
+    "##### (i) Write a fn to implement sigmoid fn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "id": "jL_TRQFG2l5m"
+   },
+   "outputs": [],
+   "source": [
+    "def sigmoidfn(x):\n",
+    "  y=1/(1+np.exp(-x))\n",
+    "  return y\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "t7KBbl7N3AUM"
+   },
+   "source": [
+    "##### (ii) Write a fn to implement derivative of sigmoid fn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "id": "okjuOpba3KOJ"
+   },
+   "outputs": [],
+   "source": [
+    "def derivative(x):\n",
+    " a=np.exp(x)\n",
+    " b=a/((1+np.exp(x))*(1+np.exp(x)))\n",
+    " return b"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "NdGTSpsp3mwr"
+   },
+   "source": [
+    "##### (iii) Test your code"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "lWY8RBex7gnd"
+   },
+   "source": [
+    "Run the following cell to check if your functions are working properly.\n",
+    "\n",
+    "*Expected output:*\n",
+    "\n",
+    "x on applying sigmoid activation fn is: [ [0.99987661 0.88079708 0.99330715 0.5        0.5       ]\n",
+    " [0.99908895 0.99330715 0.5        0.5        0.5       ] ]\n",
+    "\n",
+    "x on applying derivative of sigmoid activation fn is: [ [-1.23379350e-04 -1.04993585e-01 -6.64805667e-03 -2.50000000e-01\n",
+    "  -2.50000000e-01]\n",
+    " [-9.10221180e-04 -6.64805667e-03 -2.50000000e-01 -2.50000000e-01\n",
+    "  -2.50000000e-01] ]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "id": "ssDGpmS33vdA"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "x on applying sigmoid activation fn is: [[0.99987661 0.88079708 0.99330715 0.5        0.5       ]\n",
+      " [0.99908895 0.99330715 0.5        0.5        0.5       ]]\n",
+      "x on applying derivative of sigmoid activation fn is: [[1.23379350e-04 1.04993585e-01 6.64805667e-03 2.50000000e-01\n",
+      "  2.50000000e-01]\n",
+      " [9.10221180e-04 6.64805667e-03 2.50000000e-01 2.50000000e-01\n",
+      "  2.50000000e-01]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "x = np.array([\n",
+    "    [9,2,5,0,0],\n",
+    "    [7,5,0,0,0]\n",
+    "])\n",
+    "print(f\"x on applying sigmoid activation fn is: {sigmoidfn(x)}\")\n",
+    "print(f\"x on applying derivative of sigmoid activation fn is: {derivative(x)}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "PMa0cseyZpa1"
+   },
+   "source": [
+    "# Section 2: Exploring Pandas"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*You have been provided with a dataset which includes information about properties of superheated vapor.*\n",
+    "\n",
+    "*The dataset consists of the thermophysical properties: specific volume, specific internal energy, specific enthalpy, specific entropy of superheated vapor.*\n",
+    "\n",
+    "*Pressure is in kPa and Temperature in centigrade. In the dataframe 75, 100, 125, etc. are temperatures.*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "i5Okb_jxZ_VW"
+   },
+   "source": [
+    "### Read the csv file\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "id": "aQgvyavrcM1l"
+   },
+   "outputs": [
+    {
+     "ename": "FileNotFoundError",
+     "evalue": "[Errno 2] No such file or directory: 'superheated_vapor_properties.csv'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[3], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m df\u001b[38;5;241m=\u001b[39mpd\u001b[38;5;241m.\u001b[39mread_csv(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124msuperheated_vapor_properties.csv\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
+      "File \u001b[1;32mC:\\ProgramData\\anaconda3\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:912\u001b[0m, in \u001b[0;36mread_csv\u001b[1;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, date_format, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options, dtype_backend)\u001b[0m\n\u001b[0;32m    899\u001b[0m kwds_defaults \u001b[38;5;241m=\u001b[39m _refine_defaults_read(\n\u001b[0;32m    900\u001b[0m     dialect,\n\u001b[0;32m    901\u001b[0m     delimiter,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    908\u001b[0m     dtype_backend\u001b[38;5;241m=\u001b[39mdtype_backend,\n\u001b[0;32m    909\u001b[0m )\n\u001b[0;32m    910\u001b[0m kwds\u001b[38;5;241m.\u001b[39mupdate(kwds_defaults)\n\u001b[1;32m--> 912\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _read(filepath_or_buffer, kwds)\n",
+      "File \u001b[1;32mC:\\ProgramData\\anaconda3\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:577\u001b[0m, in \u001b[0;36m_read\u001b[1;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[0;32m    574\u001b[0m _validate_names(kwds\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnames\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m))\n\u001b[0;32m    576\u001b[0m \u001b[38;5;66;03m# Create the parser.\u001b[39;00m\n\u001b[1;32m--> 577\u001b[0m parser \u001b[38;5;241m=\u001b[39m TextFileReader(filepath_or_buffer, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwds)\n\u001b[0;32m    579\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m chunksize \u001b[38;5;129;01mor\u001b[39;00m iterator:\n\u001b[0;32m    580\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m parser\n",
+      "File \u001b[1;32mC:\\ProgramData\\anaconda3\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:1407\u001b[0m, in \u001b[0;36mTextFileReader.__init__\u001b[1;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[0;32m   1404\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m kwds[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[0;32m   1406\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles: IOHandles \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m-> 1407\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_make_engine(f, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mengine)\n",
+      "File \u001b[1;32mC:\\ProgramData\\anaconda3\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:1661\u001b[0m, in \u001b[0;36mTextFileReader._make_engine\u001b[1;34m(self, f, engine)\u001b[0m\n\u001b[0;32m   1659\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m mode:\n\u001b[0;32m   1660\u001b[0m         mode \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m-> 1661\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles \u001b[38;5;241m=\u001b[39m get_handle(\n\u001b[0;32m   1662\u001b[0m     f,\n\u001b[0;32m   1663\u001b[0m     mode,\n\u001b[0;32m   1664\u001b[0m     encoding\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mencoding\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m),\n\u001b[0;32m   1665\u001b[0m     compression\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcompression\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m),\n\u001b[0;32m   1666\u001b[0m     memory_map\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmemory_map\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mFalse\u001b[39;00m),\n\u001b[0;32m   1667\u001b[0m     is_text\u001b[38;5;241m=\u001b[39mis_text,\n\u001b[0;32m   1668\u001b[0m     errors\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mencoding_errors\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstrict\u001b[39m\u001b[38;5;124m\"\u001b[39m),\n\u001b[0;32m   1669\u001b[0m     storage_options\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstorage_options\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m),\n\u001b[0;32m   1670\u001b[0m )\n\u001b[0;32m   1671\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m   1672\u001b[0m f \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles\u001b[38;5;241m.\u001b[39mhandle\n",
+      "File \u001b[1;32mC:\\ProgramData\\anaconda3\\Lib\\site-packages\\pandas\\io\\common.py:859\u001b[0m, in \u001b[0;36mget_handle\u001b[1;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[0;32m    854\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(handle, \u001b[38;5;28mstr\u001b[39m):\n\u001b[0;32m    855\u001b[0m     \u001b[38;5;66;03m# Check whether the filename is to be opened in binary mode.\u001b[39;00m\n\u001b[0;32m    856\u001b[0m     \u001b[38;5;66;03m# Binary mode does not support 'encoding' and 'newline'.\u001b[39;00m\n\u001b[0;32m    857\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m ioargs\u001b[38;5;241m.\u001b[39mencoding \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m ioargs\u001b[38;5;241m.\u001b[39mmode:\n\u001b[0;32m    858\u001b[0m         \u001b[38;5;66;03m# Encoding\u001b[39;00m\n\u001b[1;32m--> 859\u001b[0m         handle \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mopen\u001b[39m(\n\u001b[0;32m    860\u001b[0m             handle,\n\u001b[0;32m    861\u001b[0m             ioargs\u001b[38;5;241m.\u001b[39mmode,\n\u001b[0;32m    862\u001b[0m             encoding\u001b[38;5;241m=\u001b[39mioargs\u001b[38;5;241m.\u001b[39mencoding,\n\u001b[0;32m    863\u001b[0m             errors\u001b[38;5;241m=\u001b[39merrors,\n\u001b[0;32m    864\u001b[0m             newline\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m    865\u001b[0m         )\n\u001b[0;32m    866\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    867\u001b[0m         \u001b[38;5;66;03m# Binary mode\u001b[39;00m\n\u001b[0;32m    868\u001b[0m         handle \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mopen\u001b[39m(handle, ioargs\u001b[38;5;241m.\u001b[39mmode)\n",
+      "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'superheated_vapor_properties.csv'"
+     ]
+    }
+   ],
+   "source": [
+    "df=pd.read_csv('superheated_vapor_properties.csv')\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Zp8F3rk2cNxy"
+   },
+   "source": [
+    "### Display the shape of data frame\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "id": "v3Q8kQkucgK0"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(544, 37)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "w49cp-8zcgd2"
+   },
+   "source": [
+    "### Return an array containing names of all the columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "id": "fz4HRb3JcsZp"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Index(['Pressure', 'Property', 'Liq_Sat', 'Vap_Sat', '75', '100', '125', '150',\n",
+      "       '175', '200', '220', '225', '240', '250', '260', '275', '280', '290',\n",
+      "       '300', '320', '325', '340', '350', '360', '375', '380', '400', '425',\n",
+      "       '450', '475', '500', '525', '550', '575', '600', '625', '650'],\n",
+      "      dtype='object')\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df.columns)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "YkTH9tRWcrH-"
+   },
+   "source": [
+    "### Display the number of null values in each column of the dataframe\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "id": "GK9-PJPxc3Ot"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Pressure      0\n",
+       "Property      0\n",
+       "Liq_Sat       0\n",
+       "Vap_Sat       0\n",
+       "75          528\n",
+       "100         508\n",
+       "125         488\n",
+       "150         448\n",
+       "175         384\n",
+       "200         320\n",
+       "220         408\n",
+       "225         400\n",
+       "240         408\n",
+       "250         344\n",
+       "260         384\n",
+       "275         340\n",
+       "280         380\n",
+       "290         488\n",
+       "300          60\n",
+       "320         480\n",
+       "325         136\n",
+       "340         476\n",
+       "350          68\n",
+       "360         476\n",
+       "375         204\n",
+       "380         476\n",
+       "400           0\n",
+       "425         204\n",
+       "450           0\n",
+       "475         204\n",
+       "500           0\n",
+       "525         272\n",
+       "550           0\n",
+       "575         340\n",
+       "600           0\n",
+       "625         476\n",
+       "650           0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.isnull().sum()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Av-lcft2c3mx"
+   },
+   "source": [
+    "### Create a column which contains the Pressure and Property columns, seperated with 'at' (For eg. V at 1, H at 101.325). Using this print the following:\n",
+    "- Enthalpy at 75 kPa and 573 K\n",
+    "- Entropy at 493 K and 250 kPa\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "id": "B2AihQj_c32C"
+   },
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'df' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[1], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mPropert at Pressure\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m=\u001b[39mdf[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mPressure\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mastype(\u001b[38;5;28mint\u001b[39m)\u001b[38;5;241m.\u001b[39mastype(\u001b[38;5;28mstr\u001b[39m)\u001b[38;5;241m+\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m at \u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;241m+\u001b[39mdf[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mProperty\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mastype(\u001b[38;5;28mstr\u001b[39m)\n\u001b[0;32m      2\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mEnthalpy at 75 KPa and 573K\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m      3\u001b[0m \u001b[38;5;28mprint\u001b[39m(df[(df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mPressure\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m==\u001b[39m\u001b[38;5;241m75\u001b[39m)\u001b[38;5;241m&\u001b[39m(df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mProperty\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m==\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mH\u001b[39m\u001b[38;5;124m'\u001b[39m)][\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mProperty at Pressure\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mvalues[\u001b[38;5;241m0\u001b[39m])\n",
+      "\u001b[1;31mNameError\u001b[0m: name 'df' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "df['Propert at Pressure']=df['Pressure'].astype(int).astype(str)+' at ' +df['Property'].astype(str)\n",
+    "print(\"Enthalpy at 75 KPa and 573K\")\n",
+    "print(df[(df['Pressure']==75)&(df['Property']=='H')]['Property at Pressure'].values[0])\n",
+    "print(\"Entropy at 493 K and 250 kPa\")\n",
+    "print(df[(df['Pressure']==250)&(df['Property']=='S')]['Property at Pressure'].values[0])\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "GpLtXYRLc4Ho"
+   },
+   "source": [
+    "### Find out the column with the highest number of missing values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "id": "q80Grgeyc4Xn"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Column with highest null value is 75\n"
+     ]
+    }
+   ],
+   "source": [
+    "b=df.isnull().sum()\n",
+    "max=0\n",
+    "r=df.shape[1]\n",
+    "for x in range(0,r):\n",
+    "    c=b[x]\n",
+    "    if c>max:\n",
+    "        max=c\n",
+    "        index=x\n",
+    "        \n",
+    "col=df.columns        \n",
+    "print(f\"Column with highest null value is {col[index]}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "74NVHTTac4nv"
+   },
+   "source": [
+    "### What is the average enthalpy of Sat. Liq. at all different pressures in the dataset?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "id": "g3mByhBDdpVr"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "936.9707720588235"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1=df[(df['Property']=='H')]\n",
+    "df1['Liq_Sat'].mean()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "uS-SuqU8dpxg"
+   },
+   "source": [
+    "### Separate out the V,U,H,S data from the dataset into V_data, U_data, H_data, S_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "id": "Sg3V9Toyd1Av"
+   },
+   "outputs": [],
+   "source": [
+    "V_data=df[df['Property']=='V']\n",
+    "U_data=df[df['Property']=='U']\n",
+    "H_data=df[df['Property']=='H']\n",
+    "S_data=df[df['Property']=='S']\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Ghkj0-0-d1W7"
+   },
+   "source": [
+    "# Section 3: Plotting with Matplotlib"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "ka0qrDcZeAL6"
+   },
+   "source": [
+    "### Plot the properties (specific volume, specific internal energy, specific enthalpy, specific entropy) vs Pressure for saturated liquid.\n",
+    "\n",
+    "Note:\n",
+    "- Try using the subplot feature of matplotlib(Explore it!!)\n",
+    "- Provide appropriate title, labels, markersize and other parameters to the plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "id": "XbKdm-cUePKA"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Entropy')"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
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",
+      "text/plain": [
+       "<Figure size 640x480 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from matplotlib import pyplot as pp\n",
+    "pp.subplot(2,2,1)\n",
+    "pp.plot(V_data['Pressure'],V_data['Liq_Sat'])\n",
+    "pp.xlabel(\"Pressure\")\n",
+    "pp.ylabel(\"Volume\")\n",
+    "pp.subplot(2,2,2)\n",
+    "pp.plot(U_data['Pressure'],U_data['Liq_Sat'])\n",
+    "pp.xlabel(\"Pressure\")\n",
+    "pp.ylabel(\"Internal Energy\")\n",
+    "pp.subplot(2,2,3)\n",
+    "pp.plot(H_data['Pressure'],H_data['Liq_Sat'])\n",
+    "pp.xlabel(\"Pressure\")\n",
+    "pp.ylabel(\"Enthalpy\")\n",
+    "pp.subplot(2,2,4)\n",
+    "pp.plot(S_data['Pressure'],S_data['Liq_Sat'])\n",
+    "pp.xlabel(\"Pressure\")\n",
+    "pp.ylabel(\"Entropy\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Plot the same for saturated vapor."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Entropy')"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
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",
+      "text/plain": [
+       "<Figure size 640x480 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "pp.subplot(2,2,1)\n",
+    "pp.plot(V_data['Pressure'],V_data['Vap_Sat'])\n",
+    "pp.xlabel(\"Pressure\")\n",
+    "pp.ylabel(\"Volume\")\n",
+    "pp.subplot(2,2,2)\n",
+    "pp.plot(U_data['Pressure'],U_data['Vap_Sat'])\n",
+    "pp.xlabel(\"Pressure\")\n",
+    "pp.ylabel(\"Internal Energy\")\n",
+    "pp.subplot(2,2,3)\n",
+    "pp.plot(H_data['Pressure'],H_data['Vap_Sat'])\n",
+    "pp.xlabel(\"Pressure\")\n",
+    "pp.ylabel(\"Enthalpy\")\n",
+    "pp.subplot(2,2,4)\n",
+    "pp.plot(S_data['Pressure'],S_data['Vap_Sat'])\n",
+    "pp.xlabel(\"Pressure\")\n",
+    "pp.ylabel(\"Entropy\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "SCLRji6TeQgh"
+   },
+   "source": [
+    "### Plot the specific volume of saturated liquid between 300 kPa and 1500 kPa"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "id": "u6DyyI7MeYgE"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'volume of saturated liquid between 300 kPa and 1500 kPa')"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "gn=V_data[(V_data['Pressure']>=300)&(V_data['Pressure']<=1500)]\n",
+    "pp.plot(gn['Liq_Sat'],gn['Pressure'])\n",
+    "pp.xlabel(\"Liq_Sat\")\n",
+    "pp.ylabel(\"Pressure\")\n",
+    "pp.title(\"volume of saturated liquid between 300 kPa and 1500 kPa\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Section 4 : Conclusion"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*Congratulations on reaching this point! I hope you had fun solving your first assignment and have also built confidence in applying these libraries. If you are wondering, we will cover more about z-score normalization in Week 2, and the sigmoid function will be used in Week 3. After completing this assignment, you are now prepared to learn about machine learning techniques and implement your own machine learning models.*"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "provenance": [],
+   "toc_visible": true
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/Suyash_231067/Assigment_2/Copy of AirQualityUCI.csv b/Suyash_231067/Assigment_2/Copy of AirQualityUCI.csv
new file mode 100644
index 0000000..f727c0f
--- /dev/null
+++ b/Suyash_231067/Assigment_2/Copy of AirQualityUCI.csv	
@@ -0,0 +1,9358 @@
+Date,Time,CO(GT),PT08.S1(CO),NMHC(GT),C6H6(GT),PT08.S2(NMHC),NOx(GT),PT08.S3(NOx),NO2(GT),PT08.S4(NO2),PT08.S5(O3),T,RH,AH
+10-03-2004,18:00:00,2.6,1360,150,11.9,1046,166,1056,113,1692,1268,13.6,48.9,0.7578
+10-03-2004,19:00:00,2,1292,112,9.4,955,103,1174,92,1559,972,13.3,47.7,0.7255
+10-03-2004,20:00:00,2.2,1402,88,9.0,939,131,1140,114,1555,1074,11.9,54.0,0.7502
+10-03-2004,21:00:00,2.2,1376,80,9.2,948,172,1092,122,1584,1203,11.0,60.0,0.7867
+10-03-2004,22:00:00,1.6,1272,51,6.5,836,131,1205,116,1490,1110,11.2,59.6,0.7888
+10-03-2004,23:00:00,1.2,1197,38,4.7,750,89,1337,96,1393,949,11.2,59.2,0.7848
+11-03-2004,00:00:00,1.2,1185,31,3.6,690,62,1462,77,1333,733,11.3,56.8,0.7603
+11-03-2004,01:00:00,1,1136,31,3.3,672,62,1453,76,1333,730,10.7,60.0,0.7702
+11-03-2004,02:00:00,0.9,1094,24,2.3,609,45,1579,60,1276,620,10.7,59.7,0.7648
+11-03-2004,03:00:00,0.6,1010,19,1.7,561,-200,1705,-200,1235,501,10.3,60.2,0.7517
+11-03-2004,04:00:00,-200,1011,14,1.3,527,21,1818,34,1197,445,10.1,60.5,0.7465
+11-03-2004,05:00:00,0.7,1066,8,1.1,512,16,1918,28,1182,422,11.0,56.2,0.7366
+11-03-2004,06:00:00,0.7,1052,16,1.6,553,34,1738,48,1221,472,10.5,58.1,0.7353
+11-03-2004,07:00:00,1.1,1144,29,3.2,667,98,1490,82,1339,730,10.2,59.6,0.7417
+11-03-2004,08:00:00,2,1333,64,8.0,900,174,1136,112,1517,1102,10.8,57.4,0.7408
+11-03-2004,09:00:00,2.2,1351,87,9.5,960,129,1079,101,1583,1028,10.5,60.6,0.7691
+11-03-2004,10:00:00,1.7,1233,77,6.3,827,112,1218,98,1446,860,10.8,58.4,0.7552
+11-03-2004,11:00:00,1.5,1179,43,5.0,762,95,1328,92,1362,671,10.5,57.9,0.7352
+11-03-2004,12:00:00,1.6,1236,61,5.2,774,104,1301,95,1401,664,9.5,66.8,0.7951
+11-03-2004,13:00:00,1.9,1286,63,7.3,869,146,1162,112,1537,799,8.3,76.4,0.8393
+11-03-2004,14:00:00,2.9,1371,164,11.5,1034,207,983,128,1730,1037,8.0,81.1,0.8736
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diff --git a/Suyash_231067/Assigment_2/Q1.ipynb b/Suyash_231067/Assigment_2/Q1.ipynb
new file mode 100644
index 0000000..67f44fd
--- /dev/null
+++ b/Suyash_231067/Assigment_2/Q1.ipynb
@@ -0,0 +1,466 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 752,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as  np \n",
+    "import pandas as pd \n",
+    "from matplotlib import pyplot as plt \n",
+    "import math"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "GENARATING DATA SET"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 754,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "50.18286570443969"
+      ]
+     },
+     "execution_count": 754,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rng=np.random.RandomState(10)\n",
+    "x=2*rng.rand(50)\n",
+    "y=5+2*x+rng.randn(50)\n",
+    "m=x.shape[0] \n",
+    "np.sum(x)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "PLOT OF DATA"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 755,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(x,y,\"bo\")\n",
+    "plt.xlim(0,2)\n",
+    "plt.ylim(0,15)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "COST FUNCTION 1 for linear regression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 813,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def cost1(x_out,x):\n",
+    "    cf1=(abs((x-x_out))**3)/2\n",
+    "    cff1=cf1.mean()\n",
+    "    return cff1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "COST FUNCTION 2 for polynomial regression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 681,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def cost2(y_out,y):\n",
+    "    cf2=(abs((y-y_out)**7)/2)\n",
+    "    cff2=cf2.mean()\n",
+    "    return cff2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Hypothesis 1 for linear regression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 757,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def hyp1(w,b,y):\n",
+    "    return w*y+b"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Hypothesis 2 for polynomial regression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 682,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def hyp2(a,c,x):\n",
+    "    return a*(np.log(x))+c\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Gradient Descent 1\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 833,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def gd1(x,y,w,b,m,learning_rate):\n",
+    "    x_out=hyp1(w,b,y)    \n",
+    "    dw=(3/(2*m))*np.sum(np.multiply(np.multiply(abs(x-x_out),(x-x_out)),-y))\n",
+    "    db=-(3/(2*m))*np.sum(np.multiply(abs(x-x_out),(x-x_out)))\n",
+    "    w=w-learning_rate*dw\n",
+    "    b=b-learning_rate*db\n",
+    "    return w,b"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Gradient Descent 2 for polynomial regression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 683,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def gd2(x,y,a,c,learning_rate,m):\n",
+    "    y_out=hyp2(a,c,x)\n",
+    "    x_log=np.log(x)\n",
+    "    da=(7/(2*m))*np.sum(np.multiply(np.multiply(abs(y-y_out)**5,(y-y_out)),-x_log))\n",
+    "    \n",
+    "    dc=(7/(2*m))*np.sum(np.multiply(np.multiply((abs(y-y_out)**5),(y-y_out)),-1))\n",
+    "    a=a-learning_rate*da\n",
+    "    \n",
+    "    c=c-learning_rate*dc\n",
+    "    \n",
+    "    return a,c\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Implement for linear regression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 834,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "w = np.random.randn(1)\n",
+    "b= np.random.randn(1)\n",
+    "\n",
+    "learning_rate=0.00005\n",
+    "necho=5000000\n",
+    "\n",
+    "\n",
+    "L=[]\n",
+    "for i in range(necho):\n",
+    "    w,b=gd1(x,y,w,b,m,learning_rate)\n",
+    "    x_out=hyp1(w,b,y)\n",
+    "    L.append(cost1(x_out,x))\n",
+    "    #if (i%(necho/10))==0:\n",
+    "       # plt.plot(x,y,'bo')\n",
+    "        #plt.plot(x_out,y,'g--')\n",
+    "        #plt.show()\n",
+    "        \n",
+    "        \n",
+    "        \n",
+    "        \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 830,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.0, 15.0)"
+      ]
+     },
+     "execution_count": 830,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(x,y,\"bo\")\n",
+    "plt.plot(x_out,y,\"r\")\n",
+    "plt.xlim(0,2)\n",
+    "plt.ylim(0,15)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 831,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.0, 0.1)"
+      ]
+     },
+     "execution_count": 831,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(L,\"r\")\n",
+    "plt.ylim(0,0.1)\n",
+    "#plt.xlim(0,1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "IMPLEMENT POLYNOMIAL REGRESSION"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 724,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a=np.random.randn(1)\n",
+    "B=np.random.randn(1)\n",
+    "c=np.random.randn(1)\n",
+    "d=np.random.randn(1)\n",
+    "\n",
+    "learning_rate=0.000005\n",
+    "noche=1000000\n",
+    "\n",
+    "L1=[]\n",
+    "\n",
+    "\n",
+    "for i in range(noche):\n",
+    "    a,c= gd2(x,y,a,c,learning_rate,m)\n",
+    "    y_out=hyp2(a,c,x)\n",
+    "    L1.append(cost2(y_out,y))\n",
+    "    \n",
+    "    #if (i%(necho/10))==0:\n",
+    "       # plt.plot(x,y,'bo')\n",
+    "        #plt.plot(x_out,y,'g--')\n",
+    "        #plt.show()\n",
+    "       "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 809,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.0, 11.0)"
+      ]
+     },
+     "execution_count": 809,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(x,y,\"bo\")\n",
+    "plt.plot(x,y_out,\"go\")\n",
+    "plt.ylim(0,11)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 726,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.0, 70.0)"
+      ]
+     },
+     "execution_count": 726,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(L1,\"r\")\n",
+    "plt.ylim(0,70)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "USING SCIKIT"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 832,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x2a814999340>]"
+      ]
+     },
+     "execution_count": 832,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x_train=x.reshape(-1,1)\n",
+    "y_train=y.reshape(-1,1)\n",
+    "from sklearn.linear_model import LinearRegression as LR\n",
+    "regressor=LR()\n",
+    "regressor.fit(x_train,y_train)\n",
+    "inct=regressor.intercept_\n",
+    "coff=regressor.coef_\n",
+    "X=np.linspace(0,2,50).reshape(-1,1)\n",
+    "Y=coff*X+inct\n",
+    "plt.plot(x,y,\"bo\")\n",
+    "plt.plot(X,Y,\"r\")\n",
+    "plt.plot(x_out,y,\"g\")\n",
+    "plt.plot(x,y_out,\"ro\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Suyash_231067/Assigment_2/Q2.ipynb b/Suyash_231067/Assigment_2/Q2.ipynb
new file mode 100644
index 0000000..8fee7e1
--- /dev/null
+++ b/Suyash_231067/Assigment_2/Q2.ipynb
@@ -0,0 +1,454 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 136,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.linear_model import LinearRegression as LR\n",
+    "from sklearn.model_selection import train_test_split as tts"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "IMPORTING DATA"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 121,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df=pd.read_csv('Copy of AirQualityUCI.csv')\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "MISSING VALUE"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 122,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df1=df.replace(-200,0)\n",
+    "c=np.array(df.columns)\n",
+    "c1=np.array(df1.columns)\n",
+    "for i in range(2,len(c)):\n",
+    "    df[c[i]]=df[c[i]].replace(-200,df1[c[i]].mean())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "CHECKING CORRELATION"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 123,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 123,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import seaborn as sns\n",
+    "dff=df.iloc[:,2:]\n",
+    "corr = dff.corr(method = 'pearson')\n",
+    "\n",
+    "sns.heatmap(corr,annot=True,fmt=\".2f\", linewidth=.5)\n",
+    "#plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "OUTLIERS"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 124,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#I have deleted outliers of NOx as it has highest correlation with RH\n",
+    "\n",
+    "Q1=df[\"NOx(GT)\"].quantile(0.25)\n",
+    "Q2=df[\"NOx(GT)\"].quantile(0.75)\n",
+    "upper=Q2+1.5*(Q2-Q1)\n",
+    "lower=Q2-1.5*(Q2-Q1)\n",
+    "df3=df[(df[\"NOx(GT)\"]>lower)&(df[\"NOx(GT)\"]<upper)]\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "DROPPING UNNECESSARY DATA"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 125,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 125,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df3=df3.drop(\"Date\",axis=1)\n",
+    "df3=df3.drop(\"Time\",axis=1)\n",
+    "df3=df3.drop(\"PT08.S1(CO)\",axis=1)\n",
+    "df3=df3.drop(\"PT08.S2(NMHC)\",axis=1)\n",
+    "df3=df3.drop(\"PT08.S3(NOx)\",axis=1)\n",
+    "df3=df3.drop(\"PT08.S4(NO2)\",axis=1)\n",
+    "df3=df3.drop(\"PT08.S5(O3)\",axis=1)\n",
+    "corr = df3.corr(method = 'pearson')\n",
+    "\n",
+    "sns.heatmap(corr,annot=True,fmt=\".2f\", linewidth=.5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "SPLITING DATA"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 321,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X1=df3['CO(GT)'].to_numpy().reshape(-1,1)\n",
+    "X2=df3['NMHC(GT)'].to_numpy().reshape(-1,1)\n",
+    "X3=df3['C6H6(GT)'].to_numpy().reshape(-1,1)\n",
+    "X4=df3['NOx(GT)'].to_numpy().reshape(-1,1)\n",
+    "X5=df3['NO2(GT)'].to_numpy().reshape(-1,1)\n",
+    "X6=df3['T'].to_numpy().reshape(-1,1)\n",
+    "ones=np.ones([8393,1]).reshape(-1,1)\n",
+    "\n",
+    "Y=df3['RH'].to_numpy().reshape(-1,1)\n",
+    "X=np.hstack([ones,X3,X4,X5,X6])\n",
+    "X1=np.hstack([X3,X4,X5,X6])\n",
+    "y_max=np.max(np.abs(Y))\n",
+    "x_max=np.max(np.abs(X))\n",
+    "x_1max=np.max(np.abs(X1))\n",
+    "\n",
+    "Y_=(Y-y_max)/y_max\n",
+    "X_=(X-x_max)/x_max\n",
+    "X_1=(X1-x_1max)/x_1max\n",
+    "\n",
+    "x_train1,x_test1,y_train1,y_test1=tts(X_1, Y_, test_size=0.15,  random_state =42)\n",
+    "\n",
+    "x_train,x_test,y_train,y_test=tts(X_, Y_, test_size=0.15,  random_state =42)\n",
+    "m=y_train.shape[0]\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "HYPOTHESIS as m+n*X3+o*X4+p*X5+q*X6\n",
+    "seeing very less correlation of CO and NMHC, not accounted them in hypothesis\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 297,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def hypo(theta,x_train):\n",
+    "    return x_train.dot(theta)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "COST FUNCTION"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 298,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def cost_function(y_train,y_out):\n",
+    "    \n",
+    "    l=(y_train-y_out)**2/2\n",
+    "    cf=l.mean()\n",
+    "    return cf\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "GRADIENT DESCENT"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 299,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def gd(x_train,y_train,m,theta,learning_rate):\n",
+    "    y_out=hypo(theta,x_train)\n",
+    "    dm=(1/m)*np.sum(np.multiply((y_train-y_out),-X_[0]))\n",
+    "    dn=(1/m)*np.sum(np.multiply((y_train-y_out),-X_[1]))\n",
+    "    do=(1/m)*np.sum(np.multiply((y_train-y_out),-X_[2]))\n",
+    "    dp=(1/m)*np.sum(np.multiply((y_train-y_out),-X_[3]))\n",
+    "    dq=(1/m)*np.sum(np.multiply((y_train-y_out),-X_[4]))\n",
+    "\n",
+    "    theta[0]=theta[0]-learning_rate*dm\n",
+    "    theta[1]=theta[1]-learning_rate*dn\n",
+    "    theta[2]=theta[2]-learning_rate*do\n",
+    "    theta[3]=theta[3]-learning_rate*dp\n",
+    "    theta[4]=theta[4]-learning_rate*dq\n",
+    "\n",
+    "    return theta"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 212,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1. , 0.5, 0. ])"
+      ]
+     },
+     "execution_count": 212,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x=np.array([9,8,7])\n",
+    "mx=np.max(x)\n",
+    "mn=np.min(x)\n",
+    "x=(x-mn)/(mx-mn)\n",
+    "x"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "IMPLEMENT USING GD"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 300,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "theta=np.empty((5,1))\n",
+    "\n",
+    "learning_rate=0.005\n",
+    "noche=10000\n",
+    "\n",
+    "L=[]\n",
+    "\n",
+    "for i in range(noche):\n",
+    "    theta=gd(x_train,y_train,m,theta,learning_rate)\n",
+    "    y_out=hypo(theta,x_train).reshape(-1,1)\n",
+    "    L.append(cost_function(y_train,y_out))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 303,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.0, 0.1)"
+      ]
+     },
+     "execution_count": 303,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(L,\"r\")\n",
+    "plt.ylim(0,.1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "USING SCIKIT"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 322,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "from sklearn.linear_model import LinearRegression as LR\n",
+    "regressor=LR()\n",
+    "regressor.fit(x_train1,y_train1)\n",
+    "\n",
+    "predictions=regressor.predict(x_test1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "MEAN SQUARED ERROR"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 323,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MSE of data by scikit : 0.02064605882647789\n",
+      "MSE of data by GD : 0.03946085388001057\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import mean_squared_error\n",
+    "print(\"MSE of data by scikit :\", mean_squared_error(y_test,predictions))\n",
+    "y_rs=x_test.dot(theta)\n",
+    "g=y_test.shape[0]\n",
+    "er=(1/g)*np.sum(np.multiply((y_test-y_rs),(y_test-y_rs)))\n",
+    "print(\"MSE of data by GD :\", er)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "CALCULATION OF R2 SCORE"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 333,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "R2 score of GD model: 0.9999796189689623\n",
+      "R2 score of SCIKIT model: 0.9999893365722133\n"
+     ]
+    }
+   ],
+   "source": [
+    "RSS1=mean_squared_error(y_test,predictions)*g\n",
+    "RSS=er*g\n",
+    "TSS=np.sum(np.multiply((Y-Y.mean()),(Y-Y.mean())))\n",
+    "R2=1-(RSS/TSS)\n",
+    "R2_1=1-(RSS1/TSS)\n",
+    "print(\"R2 score of GD model:\",R2)\n",
+    "print(\"R2 score of SCIKIT model:\",R2_1)\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Suyash_231067/Assignment1.ipynb b/Suyash_231067/Assignment1.ipynb
index ab3ea13..80aea74 100644
--- a/Suyash_231067/Assignment1.ipynb
+++ b/Suyash_231067/Assignment1.ipynb
@@ -31,7 +31,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 2,
    "metadata": {
     "id": "jdwX7bI7aGeY"
    },
@@ -517,11 +517,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 3,
    "metadata": {
     "id": "aQgvyavrcM1l"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "FileNotFoundError",
+     "evalue": "[Errno 2] No such file or directory: 'superheated_vapor_properties.csv'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[3], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m df\u001b[38;5;241m=\u001b[39mpd\u001b[38;5;241m.\u001b[39mread_csv(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124msuperheated_vapor_properties.csv\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
+      "File \u001b[1;32mC:\\ProgramData\\anaconda3\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:912\u001b[0m, in \u001b[0;36mread_csv\u001b[1;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, date_format, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options, dtype_backend)\u001b[0m\n\u001b[0;32m    899\u001b[0m kwds_defaults \u001b[38;5;241m=\u001b[39m _refine_defaults_read(\n\u001b[0;32m    900\u001b[0m     dialect,\n\u001b[0;32m    901\u001b[0m     delimiter,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    908\u001b[0m     dtype_backend\u001b[38;5;241m=\u001b[39mdtype_backend,\n\u001b[0;32m    909\u001b[0m )\n\u001b[0;32m    910\u001b[0m kwds\u001b[38;5;241m.\u001b[39mupdate(kwds_defaults)\n\u001b[1;32m--> 912\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _read(filepath_or_buffer, kwds)\n",
+      "File \u001b[1;32mC:\\ProgramData\\anaconda3\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:577\u001b[0m, in \u001b[0;36m_read\u001b[1;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[0;32m    574\u001b[0m _validate_names(kwds\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnames\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m))\n\u001b[0;32m    576\u001b[0m \u001b[38;5;66;03m# Create the parser.\u001b[39;00m\n\u001b[1;32m--> 577\u001b[0m parser \u001b[38;5;241m=\u001b[39m TextFileReader(filepath_or_buffer, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwds)\n\u001b[0;32m    579\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m chunksize \u001b[38;5;129;01mor\u001b[39;00m iterator:\n\u001b[0;32m    580\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m parser\n",
+      "File \u001b[1;32mC:\\ProgramData\\anaconda3\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:1407\u001b[0m, in \u001b[0;36mTextFileReader.__init__\u001b[1;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[0;32m   1404\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m kwds[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[0;32m   1406\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles: IOHandles \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m-> 1407\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_make_engine(f, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mengine)\n",
+      "File \u001b[1;32mC:\\ProgramData\\anaconda3\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:1661\u001b[0m, in \u001b[0;36mTextFileReader._make_engine\u001b[1;34m(self, f, engine)\u001b[0m\n\u001b[0;32m   1659\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m mode:\n\u001b[0;32m   1660\u001b[0m         mode \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m-> 1661\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles \u001b[38;5;241m=\u001b[39m get_handle(\n\u001b[0;32m   1662\u001b[0m     f,\n\u001b[0;32m   1663\u001b[0m     mode,\n\u001b[0;32m   1664\u001b[0m     encoding\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mencoding\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m),\n\u001b[0;32m   1665\u001b[0m     compression\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcompression\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m),\n\u001b[0;32m   1666\u001b[0m     memory_map\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmemory_map\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mFalse\u001b[39;00m),\n\u001b[0;32m   1667\u001b[0m     is_text\u001b[38;5;241m=\u001b[39mis_text,\n\u001b[0;32m   1668\u001b[0m     errors\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mencoding_errors\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstrict\u001b[39m\u001b[38;5;124m\"\u001b[39m),\n\u001b[0;32m   1669\u001b[0m     storage_options\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstorage_options\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m),\n\u001b[0;32m   1670\u001b[0m )\n\u001b[0;32m   1671\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m   1672\u001b[0m f \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles\u001b[38;5;241m.\u001b[39mhandle\n",
+      "File \u001b[1;32mC:\\ProgramData\\anaconda3\\Lib\\site-packages\\pandas\\io\\common.py:859\u001b[0m, in \u001b[0;36mget_handle\u001b[1;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[0;32m    854\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(handle, \u001b[38;5;28mstr\u001b[39m):\n\u001b[0;32m    855\u001b[0m     \u001b[38;5;66;03m# Check whether the filename is to be opened in binary mode.\u001b[39;00m\n\u001b[0;32m    856\u001b[0m     \u001b[38;5;66;03m# Binary mode does not support 'encoding' and 'newline'.\u001b[39;00m\n\u001b[0;32m    857\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m ioargs\u001b[38;5;241m.\u001b[39mencoding \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m ioargs\u001b[38;5;241m.\u001b[39mmode:\n\u001b[0;32m    858\u001b[0m         \u001b[38;5;66;03m# Encoding\u001b[39;00m\n\u001b[1;32m--> 859\u001b[0m         handle \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mopen\u001b[39m(\n\u001b[0;32m    860\u001b[0m             handle,\n\u001b[0;32m    861\u001b[0m             ioargs\u001b[38;5;241m.\u001b[39mmode,\n\u001b[0;32m    862\u001b[0m             encoding\u001b[38;5;241m=\u001b[39mioargs\u001b[38;5;241m.\u001b[39mencoding,\n\u001b[0;32m    863\u001b[0m             errors\u001b[38;5;241m=\u001b[39merrors,\n\u001b[0;32m    864\u001b[0m             newline\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m    865\u001b[0m         )\n\u001b[0;32m    866\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    867\u001b[0m         \u001b[38;5;66;03m# Binary mode\u001b[39;00m\n\u001b[0;32m    868\u001b[0m         handle \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mopen\u001b[39m(handle, ioargs\u001b[38;5;241m.\u001b[39mmode)\n",
+      "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'superheated_vapor_properties.csv'"
+     ]
+    }
+   ],
    "source": [
     "df=pd.read_csv('superheated_vapor_properties.csv')\n"
    ]
@@ -669,19 +686,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 80,
+   "execution_count": 1,
    "metadata": {
     "id": "B2AihQj_c32C"
    },
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Enthalpy at 75 KPa and 573K\n",
-      "75.0 at H\n",
-      "Entropy at 493 K and 250 kPa\n",
-      "250.0 at S\n"
+     "ename": "NameError",
+     "evalue": "name 'df' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[1], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mPropert at Pressure\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m=\u001b[39mdf[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mPressure\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mastype(\u001b[38;5;28mint\u001b[39m)\u001b[38;5;241m.\u001b[39mastype(\u001b[38;5;28mstr\u001b[39m)\u001b[38;5;241m+\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m at \u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;241m+\u001b[39mdf[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mProperty\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mastype(\u001b[38;5;28mstr\u001b[39m)\n\u001b[0;32m      2\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mEnthalpy at 75 KPa and 573K\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m      3\u001b[0m \u001b[38;5;28mprint\u001b[39m(df[(df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mPressure\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m==\u001b[39m\u001b[38;5;241m75\u001b[39m)\u001b[38;5;241m&\u001b[39m(df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mProperty\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m==\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mH\u001b[39m\u001b[38;5;124m'\u001b[39m)][\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mProperty at Pressure\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mvalues[\u001b[38;5;241m0\u001b[39m])\n",
+      "\u001b[1;31mNameError\u001b[0m: name 'df' is not defined"
      ]
     }
    ],
@@ -690,7 +708,8 @@
     "print(\"Enthalpy at 75 KPa and 573K\")\n",
     "print(df[(df['Pressure']==75)&(df['Property']=='H')]['Property at Pressure'].values[0])\n",
     "print(\"Entropy at 493 K and 250 kPa\")\n",
-    "print(df[(df['Pressure']==250)&(df['Property']=='S')]['Property at Pressure'].values[0])"
+    "print(df[(df['Pressure']==250)&(df['Property']=='S')]['Property at Pressure'].values[0])\n",
+    "df"
    ]
   },
   {