diff --git a/02_activities/assignments/assignment_1.ipynb b/02_activities/assignments/assignment_1.ipynb index b0a47da71..55746159b 100644 --- a/02_activities/assignments/assignment_1.ipynb +++ b/02_activities/assignments/assignment_1.ipynb @@ -34,7 +34,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 34, "id": "4a3485d6-ba58-4660-a983-5680821c5719", "metadata": {}, "outputs": [], @@ -56,10 +56,288 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 35, "id": "a431d282-f9ca-4d5d-8912-71ffc9d8ea19", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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178 rows × 14 columns

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" + ], + "text/plain": [ + " alcohol malic_acid ash alcalinity_of_ash magnesium total_phenols \\\n", + "0 14.23 1.71 2.43 15.6 127.0 2.80 \n", + "1 13.20 1.78 2.14 11.2 100.0 2.65 \n", + "2 13.16 2.36 2.67 18.6 101.0 2.80 \n", + "3 14.37 1.95 2.50 16.8 113.0 3.85 \n", + "4 13.24 2.59 2.87 21.0 118.0 2.80 \n", + ".. ... ... ... ... ... ... \n", + "173 13.71 5.65 2.45 20.5 95.0 1.68 \n", + "174 13.40 3.91 2.48 23.0 102.0 1.80 \n", + "175 13.27 4.28 2.26 20.0 120.0 1.59 \n", + "176 13.17 2.59 2.37 20.0 120.0 1.65 \n", + "177 14.13 4.10 2.74 24.5 96.0 2.05 \n", + "\n", + " flavanoids nonflavanoid_phenols proanthocyanins color_intensity hue \\\n", + "0 3.06 0.28 2.29 5.64 1.04 \n", + "1 2.76 0.26 1.28 4.38 1.05 \n", + "2 3.24 0.30 2.81 5.68 1.03 \n", + "3 3.49 0.24 2.18 7.80 0.86 \n", + "4 2.69 0.39 1.82 4.32 1.04 \n", + ".. ... ... ... ... ... \n", + "173 0.61 0.52 1.06 7.70 0.64 \n", + "174 0.75 0.43 1.41 7.30 0.70 \n", + "175 0.69 0.43 1.35 10.20 0.59 \n", + "176 0.68 0.53 1.46 9.30 0.60 \n", + "177 0.76 0.56 1.35 9.20 0.61 \n", + "\n", + " od280/od315_of_diluted_wines proline class \n", + "0 3.92 1065.0 0 \n", + "1 3.40 1050.0 0 \n", + "2 3.17 1185.0 0 \n", + "3 3.45 1480.0 0 \n", + "4 2.93 735.0 0 \n", + ".. ... ... ... \n", + "173 1.74 740.0 2 \n", + "174 1.56 750.0 2 \n", + "175 1.56 835.0 2 \n", + "176 1.62 840.0 2 \n", + "177 1.60 560.0 2 \n", + "\n", + "[178 rows x 14 columns]" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "from sklearn.datasets import load_wine\n", "\n", @@ -91,12 +369,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 36, "id": "56916892", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "178\n" + ] + } + ], "source": [ - "# Your answer here" + "# Your answer here\n", + "print(wine_df.shape[0])" ] }, { @@ -109,12 +396,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 37, "id": "df0ef103", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "14\n" + ] + } + ], "source": [ - "# Your answer here" + "# Your answer here\n", + "print(wine_df.shape[1])" ] }, { @@ -127,12 +423,31 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 38, "id": "47989426", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "int64\n", + "[0 1 2]\n" + ] + } + ], "source": [ - "# Your answer here" + "# Your answer here\n", + "print(wine_df['class'].dtype)\n", + "print(wine_df['class'].unique())" + ] + }, + { + "cell_type": "markdown", + "id": "8e29ae15", + "metadata": {}, + "source": [ + "Class is a Categorical Variable, represented here in integers (0,1,2) stored in our Dataframe as int64." ] }, { @@ -146,12 +461,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "id": "bd7b0910", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "13\n" + ] + } + ], "source": [ - "# Your answer here" + "# Your answer here\n", + "print(wine_df.shape[1] - 1)" ] }, { @@ -175,10 +499,37 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 40, "id": "cc899b59", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " alcohol malic_acid ash alcalinity_of_ash magnesium \\\n", + "0 1.518613 -0.562250 0.232053 -1.169593 1.913905 \n", + "1 0.246290 -0.499413 -0.827996 -2.490847 0.018145 \n", + "2 0.196879 0.021231 1.109334 -0.268738 0.088358 \n", + "3 1.691550 -0.346811 0.487926 -0.809251 0.930918 \n", + "4 0.295700 0.227694 1.840403 0.451946 1.281985 \n", + "\n", + " total_phenols flavanoids nonflavanoid_phenols proanthocyanins \\\n", + "0 0.808997 1.034819 -0.659563 1.224884 \n", + "1 0.568648 0.733629 -0.820719 -0.544721 \n", + "2 0.808997 1.215533 -0.498407 2.135968 \n", + "3 2.491446 1.466525 -0.981875 1.032155 \n", + "4 0.808997 0.663351 0.226796 0.401404 \n", + "\n", + " color_intensity hue od280/od315_of_diluted_wines proline \n", + "0 0.251717 0.362177 1.847920 1.013009 \n", + "1 -0.293321 0.406051 1.113449 0.965242 \n", + "2 0.269020 0.318304 0.788587 1.395148 \n", + "3 1.186068 -0.427544 1.184071 2.334574 \n", + "4 -0.319276 0.362177 0.449601 -0.037874 \n" + ] + } + ], "source": [ "# Select predictors (excluding the last column)\n", "predictors = wine_df.iloc[:, :-1]\n", @@ -204,7 +555,8 @@ "id": "403ef0bb", "metadata": {}, "source": [ - "> Your answer here..." + "> Your answer here...\n", + "we standardize predictor variables since knn is distance based and larger scales can dominate the prediction process. standardization is therefore done to allow the model to consider each variable based on it's relationship to the outcome and not the scale." ] }, { @@ -220,7 +572,8 @@ "id": "fdee5a15", "metadata": {}, "source": [ - "> Your answer here..." + "> Your answer here...\n", + "class is a response variable and distances are not being computed on it. Standardizing it would distort the class labels (0, 1, 2) and break the classifier." ] }, { @@ -236,7 +589,10 @@ "id": "f0676c21", "metadata": {}, "source": [ - "> Your answer here..." + "#Your answer here...\n", + "np.random.seed(42)\n", + "-setting a seed is important as it ensures that random numbers stay the same each time we. It ensures that we have consistent results for testing. \n", + "Does the value matter? No ; any integer works, What matters is that anyone reproducing your work use the same value, not which value it is. if a seed value is not set, we have different results every time we run it" ] }, { @@ -251,17 +607,35 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 41, "id": "72c101f2", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "X_train shape: (133, 13)\n", + "X_test shape: (45, 13)\n", + "y_train shape: (133,)\n", + "y_test shape: (45,)\n" + ] + } + ], "source": [ "# set a seed for reproducibility\n", - "np.random.seed(123)\n", + "np.random.seed(42)\n", + "#split the data into a training and testing set. hint: use train_test_split !\n", "\n", - "# split the data into a training and testing set. hint: use train_test_split !\n", + "# Your code here ...\n", "\n", - "# Your code here ..." + "X_train, X_test, y_train, y_test = train_test_split(\n", + " predictors_standardized, wine_df['class'], test_size=0.25, random_state=42\n", + ")\n", + "print(\"X_train shape:\", X_train.shape)\n", + "print(\"X_test shape:\", X_test.shape)\n", + "print(\"y_train shape:\", y_train.shape)\n", + "print(\"y_test shape:\", y_test.shape)" ] }, { @@ -284,12 +658,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 42, "id": "08818c64", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Best n_neighbors: 15\n" + ] + } + ], "source": [ - "# Your code here..." + "\n", + "# Your code here...\n", + "knn = KNeighborsClassifier()\n", + "\n", + "param_grid = {'n_neighbors': range(1, 51)}\n", + "\n", + "grid_search = GridSearchCV(knn, param_grid, cv=10, scoring='accuracy')\n", + "grid_search.fit(X_train, y_train)\n", + "\n", + "best_k = grid_search.best_params_['n_neighbors']\n", + "print(f\"Best n_neighbors: {best_k}\")" ] }, { @@ -305,12 +697,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 43, "id": "ffefa9f2", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Test Accuracy: 0.9556\n" + ] + } + ], "source": [ - "# Your code here..." + "# Your code here...\n", + "best_knn = KNeighborsClassifier(n_neighbors=best_k)\n", + "best_knn.fit(X_train, y_train)\n", + "\n", + "y_pred = best_knn.predict(X_test)\n", + "print(f\"Test Accuracy: {accuracy_score(y_test, y_pred):.4f}\")" ] }, { @@ -365,7 +770,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3.10.4", + "display_name": "lcr-env (3.11.15)", "language": "python", "name": "python3" }, @@ -379,12 +784,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.19" - }, - "vscode": { - "interpreter": { - "hash": "497a84dc8fec8cf8d24e7e87b6d954c9a18a327edc66feb9b9ea7e9e72cc5c7e" - } + "version": "3.11.15" } }, "nbformat": 4,