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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": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "D_bzcLusjSO0"
+   },
+   "source": [
+    "*Let's begin by importing numpy, pandas and matplotlib.*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {
+    "id": "jdwX7bI7aGeY"
+   },
+   "outputs": [],
+   "source": [
+    "#your code here\n",
+    "\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n"
+   ]
+  },
+  {
+   "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": 19,
+   "metadata": {
+    "id": "0qC4txIss3gG"
+   },
+   "outputs": [],
+   "source": [
+    "def isValid(A,B):\n",
+    "  #your code here\n",
+    " a_shape = A.shape\n",
+    " b_shape = B.shape\n",
+    " if a_shape[1] == b_shape[0]:\n",
+    "  return 1\n",
+    " else: 0\n",
+    " \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "OPTfRGlTdXxa"
+   },
+   "source": [
+    "##### (ii) Using loops (without using numpy)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {
+    "id": "V8F5ETsRct3S"
+   },
+   "outputs": [],
+   "source": [
+    "def matrix_multiply(A,B):\n",
+    "  #your code here\n",
+    "    A.tolist()\n",
+    "    B.tolist()\n",
+    "    p = len(A)\n",
+    "    q = len(A[0])\n",
+    "    r = len(B[0])\n",
+    "    C = []\n",
+    "    for a in range(p):\n",
+    "        ans = []\n",
+    "        for b in range(r):\n",
+    "            ans.append(0)\n",
+    "        C.append(ans)\n",
+    " \n",
+    "    for i in range(p):\n",
+    "        for j in range(r):\n",
+    "            ans2 = 0\n",
+    "            for k in range(q):\n",
+    "                ans2 += A[i][k]*B[k][j]\n",
+    "            C[i][j] = ans2\n",
+    " \n",
+    "    return(np.array(C))\n",
+    "    \n",
+    "    "
+   ]
+  },
+  {
+   "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",
+    "  #your code here\n",
+    "    product = np.dot(A , B)\n",
+    "    return product"
+   ]
+  },
+  {
+   "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": 56,
+   "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": 6,
+   "metadata": {
+    "id": "0vix8qaaghwP"
+   },
+   "outputs": [],
+   "source": [
+    "def mean(x):\n",
+    "  #your code here\n",
+    "  sum_of_elements = sum(x)\n",
+    "  mean1 = sum_of_elements / len(x)\n",
+    "  return mean1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "id": "j1qabel-g_f5"
+   },
+   "outputs": [],
+   "source": [
+    "def standard_deviation(x):\n",
+    "  #your code here\n",
+    "  mean1 = mean(x)\n",
+    "  squared_diff = [(elem - mean1)**2 for elem in x]\n",
+    "  variance = sum(squared_diff) / len(x)\n",
+    "  std_deviation = variance ** 0.5\n",
+    "  return std_deviation\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "id": "797ewbNqhEpY"
+   },
+   "outputs": [],
+   "source": [
+    "def zscore_normalisation(x):\n",
+    "  #your code here\n",
+    "  mean1 = mean(x)\n",
+    "  std_deviation = standard_deviation(x)\n",
+    "  normalized_values = [((elem - mean1)/std_deviation) for elem in x]\n",
+    "  return normalized_values\n",
+    "\n",
+    "\n",
+    "   "
+   ]
+  },
+  {
+   "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": 3,
+   "metadata": {
+    "id": "WjxJTUDihsYg"
+   },
+   "outputs": [],
+   "source": [
+    "def zscore_normalisation_2(x):\n",
+    "#your code here\n",
+    " import numpy as np\n",
+    " mean1 = np.mean(x)\n",
+    " std_deviation = np.std(x)\n",
+    " normalized_values = [((elem - mean1) / std_deviation) for elem in x]\n",
+    " return normalized_values\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "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": 7,
+   "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: [-1.0675326683028088, -0.9974539373420117, -0.9974539373420117, -0.8105773214465528, -0.41346451266870277, -0.06307085786471743, 0.3106823739262003, 0.9180313755864415, 1.2217058764165623, 1.8991336090376005]\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": 57,
+   "metadata": {
+    "id": "jL_TRQFG2l5m"
+   },
+   "outputs": [],
+   "source": [
+    "import math\n",
+    "\n",
+    "\n",
+    "def sigmoidfn(x):\n",
+    "  #your code here\n",
+    "    import math\n",
+    "def sigmoidfn(x):\n",
+    "  ans = 1/(1+(math.e)**(-x))\n",
+    "  return ans\n",
+    "     "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "t7KBbl7N3AUM"
+   },
+   "source": [
+    "##### (ii) Write a fn to implement derivative of sigmoid fn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {
+    "id": "okjuOpba3KOJ"
+   },
+   "outputs": [],
+   "source": [
+    "def derivative(x):\n",
+    "  #your code here\n",
+    "  ans1= ((math.e)**(x))/(1+(math.e)**(x))**2\n",
+    "  return ans1"
+   ]
+  },
+  {
+   "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": 61,
+   "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": 23,
+   "metadata": {
+    "id": "aQgvyavrcM1l"
+   },
+   "outputs": [],
+   "source": [
+    "#your code here\n",
+    "df = pd.read_csv('superheated_vapor_properties.csv')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Zp8F3rk2cNxy"
+   },
+   "source": [
+    "### Display the shape of data frame\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "id": "v3Q8kQkucgK0"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(544, 37)\n"
+     ]
+    }
+   ],
+   "source": [
+    "#your code here\n",
+    "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": 25,
+   "metadata": {
+    "id": "fz4HRb3JcsZp"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['Pressure' 'Property' 'Liq_Sat' 'Vap_Sat' '75' '100' '125' '150' '175'\n",
+      " '200' '220' '225' '240' '250' '260' '275' '280' '290' '300' '320' '325'\n",
+      " '340' '350' '360' '375' '380' '400' '425' '450' '475' '500' '525' '550'\n",
+      " '575' '600' '625' '650']\n"
+     ]
+    }
+   ],
+   "source": [
+    "#your code here\n",
+    "columns_array = df.columns.to_numpy()\n",
+    "print(columns_array)"
+   ]
+  },
+  {
+   "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": 27,
+   "metadata": {
+    "id": "GK9-PJPxc3Ot"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "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\n"
+     ]
+    }
+   ],
+   "source": [
+    "#your code here\n",
+    "no_of_null_values_in_each_column = df.isnull().sum()\n",
+    "print(no_of_null_values_in_each_column)\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": 53,
+   "metadata": {
+    "id": "B2AihQj_c32C"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   Property at Pressure     300\n",
+      "26            H at 75.0  3075.1\n",
+      "   Property at Pressure  220\n",
+      "59           S at 250.0  NaN\n"
+     ]
+    }
+   ],
+   "source": [
+    "#your code here\n",
+    "df['Property at Pressure']=df['Property'] +\" at \"+ df['Pressure'].astype('str')\n",
+    "df2=df[['Property at Pressure','300']]\n",
+    "df3=df2[(df['Property at Pressure']==\"H at 75.0\")]\n",
+    "print(df3)\n",
+    "\n",
+    "df4=df[['Property at Pressure','220']]\n",
+    "df5=df4[(df['Property at Pressure']==\"S at 250.0\")]\n",
+    "print(df5)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "GpLtXYRLc4Ho"
+   },
+   "source": [
+    "### Find out the column with the highest number of missing values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "id": "q80Grgeyc4Xn"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "75\n"
+     ]
+    }
+   ],
+   "source": [
+    "#your code here\n",
+    "column_with_max_null_values = df.isnull().sum().idxmax()\n",
+    "print(column_with_max_null_values)"
+   ]
+  },
+  {
+   "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": 54,
+   "metadata": {
+    "id": "g3mByhBDdpVr"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2.452675"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#your code here\n",
+    "df1=df[['Property','Liq_Sat']]\n",
+    "df2=df1[(df['Property']==\"S\")]\n",
+    "df2['Liq_Sat'].mean(axis = 0, skipna = True) \n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "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": 32,
+   "metadata": {
+    "id": "Sg3V9Toyd1Av"
+   },
+   "outputs": [],
+   "source": [
+    "#your code here\n",
+    "V_data = pd.DataFrame()\n",
+    "U_data = pd.DataFrame()\n",
+    "H_data = pd.DataFrame()\n",
+    "S_data = pd.DataFrame()\n",
+    "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",
+    "\n",
+    "\n",
+    "\n",
+    "\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": 44,
+   "metadata": {
+    "id": "XbKdm-cUePKA"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Entropy')"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1100x1300 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#your code here\n",
+    "plt.figure(figsize=(11,13))\n",
+    "plt.subplot(2,2,1)\n",
+    "plt.plot(V_data['Pressure'],V_data['Liq_Sat'])\n",
+    "plt.xlabel('Pressure')\n",
+    "plt.ylabel('volumne')\n",
+    "plt.subplot(2,2,2)\n",
+    "plt.plot(U_data['Pressure'],U_data['Liq_Sat'])\n",
+    "plt.xlabel('Pressure')\n",
+    "plt.ylabel('Internal Energy')\n",
+    "plt.subplot(2,2,3)\n",
+    "plt.plot(H_data['Pressure'],H_data['Liq_Sat'])\n",
+    "plt.xlabel('Pressure')\n",
+    "plt.ylabel('Enthalpy')\n",
+    "plt.subplot(2,2,4)\n",
+    "plt.plot(S_data['Pressure'],S_data['Liq_Sat'])\n",
+    "plt.xlabel('Pressure')\n",
+    "plt.ylabel('Entropy')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Plot the same for saturated vapor."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Entropy')"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1100x1300 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#your code here\n",
+    "plt.figure(figsize=(11,13))\n",
+    "plt.subplot(2,2,1)\n",
+    "plt.plot(V_data['Pressure'],V_data['Vap_Sat'])\n",
+    "plt.xlabel('Pressure')\n",
+    "plt.ylabel('volumne')\n",
+    "plt.subplot(2,2,2)\n",
+    "plt.plot(U_data['Pressure'],U_data['Vap_Sat'])\n",
+    "plt.xlabel('Pressure')\n",
+    "plt.ylabel('Internal Energy')\n",
+    "plt.subplot(2,2,3)\n",
+    "plt.plot(H_data['Pressure'],H_data['Vap_Sat'])\n",
+    "plt.xlabel('Pressure')\n",
+    "plt.ylabel('Enthalpy')\n",
+    "plt.subplot(2,2,4)\n",
+    "plt.plot(S_data['Pressure'],S_data['Vap_Sat'])\n",
+    "plt.xlabel('Pressure')\n",
+    "plt.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": 52,
+   "metadata": {
+    "id": "u6DyyI7MeYgE"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'V vs Pressure')"
+      ]
+     },
+     "execution_count": 52,
+     "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": [
+    "#your code here\n",
+    "a=V_data[(V_data['Pressure']>300) & (V_data['Pressure']<1500)]\n",
+    "X=a['Pressure']\n",
+    "Y=a['Liq_Sat']\n",
+    "plt.plot(X,Y)\n",
+    "plt.xlabel(\"Pressure(kPa)\")\n",
+    "plt.ylabel(\"Specific Volume\")\n",
+    "plt.title(\"V vs Pressure\")"
+   ]
+  },
+  {
+   "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.*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  }
+ ],
+ "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": "3.11.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}