diff --git a/02_activities/assignments/assignment_1.ipynb b/02_activities/assignments/assignment_1.ipynb index e50cc66eb..9b5aa6869 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": 58, "id": "4a3485d6-ba58-4660-a983-5680821c5719", "metadata": {}, "outputs": [], @@ -56,10 +56,288 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 59, "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": 59, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "from sklearn.datasets import load_wine\n", "\n", @@ -91,12 +369,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 60, "id": "56916892", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "178" + ] + }, + "execution_count": 60, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# Your answer here" + "# Your answer here\n", + "wine_df.shape[0]" ] }, { @@ -109,12 +399,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 61, "id": "df0ef103", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "14" + ] + }, + "execution_count": 61, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# Your answer here" + "# Your answer here\n", + "wine_df.shape[1]" ] }, { @@ -127,12 +429,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 62, "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())" ] }, { @@ -146,12 +459,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 63, "id": "bd7b0910", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "13" + ] + }, + "execution_count": 63, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# Your answer here" + "# Your answer here\n", + "wine_df.shape[1] - 1" ] }, { @@ -175,10 +500,37 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 64, "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 +556,7 @@ "id": "403ef0bb", "metadata": {}, "source": [ - "> Your answer here..." + "> KNN works by measuring the distance between points. If one feature has much bigger numbers than the others, it can completely overshadow the rest. By standardizing, we make sure all the features are on the same scale so they each contribute fairly to the model." ] }, { @@ -220,7 +572,7 @@ "id": "fdee5a15", "metadata": {}, "source": [ - "> Your answer here..." + "> The response variable is just the label of the wine type (0, 1, 2). It doesn’t represent a quantity, so scaling it wouldn’t make sense. We only scale the input features, not the categories we’re trying to predict." ] }, { @@ -236,7 +588,15 @@ "id": "f0676c21", "metadata": {}, "source": [ - "> Your answer here..." + "> random.seed(123)\n" + ] + }, + { + "cell_type": "markdown", + "id": "76ea37cf", + "metadata": {}, + "source": [ + "> Setting a seed makes sure we always get the same random split of the data when we rerun the code. That way, results are reproducible. The actual number does not matter, what matters is that we are consistent." ] }, { @@ -251,7 +611,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 65, "id": "72c101f2", "metadata": {}, "outputs": [], @@ -261,7 +621,8 @@ "\n", "# split the data into a training and testing set. hint: use train_test_split !\n", "\n", - "# Your code here ..." + "# Your code here ...\n", + "X_train, X_test, y_train, y_test = train_test_split(predictors_standardized, wine_df['class'], test_size=0.25, random_state=123)\n" ] }, { @@ -284,12 +645,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 66, "id": "08818c64", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "15\n" + ] + } + ], "source": [ - "# Your code here..." + "# Your code here...\n", + "best_n = GridSearchCV(KNeighborsClassifier(), {'n_neighbors': range(1,51)}, cv=10).fit(X_train, y_train).best_params_['n_neighbors']\n", + "\n", + "print(best_n)" ] }, { @@ -305,12 +677,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 67, "id": "ffefa9f2", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "0.9333333333333333" + ] + }, + "execution_count": 67, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# Your code here..." + "# Your code here...\n", + "accuracy_score(y_test, KNeighborsClassifier(n_neighbors=best_n).fit(X_train, y_train).predict(X_test))" ] }, { @@ -365,7 +749,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3.10.4", + "display_name": "dsi_participant", "language": "python", "name": "python3" }, @@ -380,11 +764,6 @@ "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.19" - }, - "vscode": { - "interpreter": { - "hash": "497a84dc8fec8cf8d24e7e87b6d954c9a18a327edc66feb9b9ea7e9e72cc5c7e" - } } }, "nbformat": 4,