diff --git a/02_activities/assignments/assignment_1.ipynb b/02_activities/assignments/assignment_1.ipynb index 28d4df017..b231df14b 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": 2, "id": "4a3485d6-ba58-4660-a983-5680821c5719", "metadata": {}, "outputs": [], @@ -56,10 +56,288 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "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": 3, + "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": 5, "id": "56916892", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "178\n" + ] + } + ], "source": [ - "# Your answer here" + "num_obeservations= wine_df.shape[0]\n", + "print(num_obeservations)" ] }, { @@ -109,12 +396,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "df0ef103", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "14\n" + ] + } + ], "source": [ - "# Your answer here" + "num_obeservations_col= wine_df.shape[1]\n", + "print(num_obeservations_col)" ] }, { @@ -127,12 +423,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "id": "47989426", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "array([0, 1, 2])" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# Your answer here" + "# The class variable is categorical. \n", + "wine_df['class'].unique()\n", + "#There are three unique levels, 0, 1 and 2. " ] }, { @@ -151,7 +460,7 @@ "metadata": {}, "outputs": [], "source": [ - "# Your answer here" + "# 13 variables" ] }, { @@ -175,10 +484,37 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "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 +540,7 @@ "id": "403ef0bb", "metadata": {}, "source": [ - "> Your answer here..." + "> It is important because variable standardization ensures fair contribution from all variables (all variable will have a mean of 0 and sd of 1), so that the scale of the variables will not affect the model or prediction process." ] }, { @@ -220,7 +556,7 @@ "id": "fdee5a15", "metadata": {}, "source": [ - "> Your answer here..." + "> Because Class is a response variable, not a predictor variables. Class is what we are trying to predict and it's categorical, therefore does not need standardization. " ] }, { @@ -236,7 +572,8 @@ "id": "f0676c21", "metadata": {}, "source": [ - "> Your answer here..." + "> Setting a seed is important because it allows reproducibility. If we don't set a seed, then each run will be different, for example splitting data will give us different training and testing set every time. The particular seed value is not important because as long as we keep the same seed value, the results will be reproducible.\n", + "np.random.seed(123)" ] }, { @@ -251,7 +588,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "id": "72c101f2", "metadata": {}, "outputs": [], @@ -260,8 +597,7 @@ "np.random.seed(123)\n", "\n", "# split the data into a training and testing set. hint: use train_test_split !\n", - "\n", - "# Your code here ..." + "wine_x_train,wine_x_test,wine_y_train,wine_y_test = train_test_split (predictors_standardized, wine_df['class'], train_size=0.75,shuffle= True, stratify = wine_df ['class'] )" ] }, { @@ -289,7 +625,29 @@ "metadata": {}, "outputs": [], "source": [ - "# Your code here..." + "np.random.seed(123)\n", + "\n", + "knn= KNeighborsClassifier(n_neighbors= 5) \n", + "\n", + "parameter_grid= {\n", + " \"n_neighbors\" : range(1,51)\n", + "}\n", + "\n", + "grid_search = GridSearchCV (\n", + " estimator=knn, \n", + " param_grid= parameter_grid,\n", + " cv= 10\n", + ")\n", + "\n", + "grid_search.fit (\n", + " wine_x_train,\n", + " wine_y_train\n", + ")\n", + "\n", + "accuracy_grid = pd.DataFrame (grid_search.cv_results_)\n", + "accuracy_grid\n", + "\n", + "best_n_neighbors= grid_search.best_params_['n_neighbors']" ] }, { @@ -308,9 +666,31 @@ "execution_count": null, "id": "ffefa9f2", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "0.9333333333333333" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# Your code here..." + "np.random.seed(123)\n", + "\n", + "knn_best= KNeighborsClassifier(n_neighbors= best_n_neighbors)\n", + "\n", + "knn_best.fit(\n", + " wine_x_train, wine_y_train\n", + ")\n", + "\n", + "wine_y_predict = knn_best.predict(wine_x_test)\n", + "\n", + "accuracy_score (wine_y_test, wine_y_predict)\n", + "\n" ] }, { @@ -365,7 +745,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3.10.4", + "display_name": "lcr-env", "language": "python", "name": "python3" }, @@ -379,12 +759,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.19" - }, - "vscode": { - "interpreter": { - "hash": "497a84dc8fec8cf8d24e7e87b6d954c9a18a327edc66feb9b9ea7e9e72cc5c7e" - } + "version": "3.11.13" } }, "nbformat": 4,