diff --git a/02_activities/assignments/assignment_1.ipynb b/02_activities/assignments/assignment_1.ipynb index 73d92a3ee..3ef45399d 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": 1, "id": "4a3485d6-ba58-4660-a983-5680821c5719", "metadata": {}, "outputs": [], @@ -56,10 +56,288 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "a431d282-f9ca-4d5d-8912-71ffc9d8ea19", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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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": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "from sklearn.datasets import load_wine\n", "\n", @@ -91,12 +369,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "56916892", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "178" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# Your answer here" + "len(wine_df)" ] }, { @@ -109,12 +398,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "id": "df0ef103", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "14" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# Your answer here" + "len(wine_df.columns)" ] }, { @@ -130,9 +430,20 @@ "execution_count": null, "id": "47989426", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "(dtype('int64'), array([0, 1, 2]))" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# Your answer here" + "wine_df[\"class\"].dtype, wine_df[\"class\"].unique()" ] }, { @@ -149,9 +460,20 @@ "execution_count": null, "id": "bd7b0910", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "13" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# Your answer here" + "len(wine_df.columns)-1" ] }, { @@ -178,7 +500,34 @@ "execution_count": null, "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 +553,7 @@ "id": "403ef0bb", "metadata": {}, "source": [ - "> Your answer here..." + "It is important for distance calculation since larger scales can dominate the distance calculation." ] }, { @@ -220,7 +569,7 @@ "id": "fdee5a15", "metadata": {}, "source": [ - "> Your answer here..." + "The response variable is a categorical variable and standardization does not affect its semantics hence no needed" ] }, { @@ -236,7 +585,7 @@ "id": "f0676c21", "metadata": {}, "source": [ - "> Your answer here..." + "Setting a random seed ensures reproducibility of results, so in case of running again we get the same numbers." ] }, { @@ -256,12 +605,14 @@ "metadata": {}, "outputs": [], "source": [ - "# set a seed for reproducibility\n", + "# Do not touch\n", "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 ..." + "# Create a random vector of True and False values to split the data\n", + "split = np.random.choice([True, False], size=len(predictors_standardized), replace=True, p=[0.75, 0.25])\n", + "X_train = predictors_standardized[split]\n", + "X_test = predictors_standardized[~split]\n", + "y_train = wine_df['class'][split]\n", + "y_test = wine_df['class'][~split]" ] }, { @@ -284,12 +635,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "id": "08818c64", "metadata": {}, "outputs": [], "source": [ - "# Your code here..." + "knn = KNeighborsClassifier()\n", + "param_grid = {'n_neighbors': range(1, 51)}\n", + "grid_search = GridSearchCV(knn, param_grid, cv=10, scoring='accuracy')\n", + "grid_search.fit(X_train, y_train)\n", + "\n", + "best_n_neighbors = grid_search.best_params_['n_neighbors']\n", + "best_n_neighbors" ] }, { @@ -305,12 +662,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "id": "ffefa9f2", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "0.9473684210526315" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# Your code here..." + "knn_best = KNeighborsClassifier(n_neighbors=best_n_neighbors)\n", + "knn_best.fit(X_train, y_train)\n", + "y_pred = knn_best.predict(X_test)\n", + "test_accuracy = accuracy_score(y_test, y_pred)\n", + "test_accuracy" ] }, { @@ -346,11 +718,11 @@ "If you like, you may collaborate with others in the cohort. If you choose to do so, please indicate with whom you have worked with in your pull request by tagging their GitHub username. Separate submissions are required.\n", "\n", "### Submission Parameters:\n", - "* Submission Due Date: `HH:MM AM/PM - DD/MM/YYYY`\n", + "* Submission Due Date: `11:59 PM - 01/12/2025`\n", "* The branch name for your repo should be: `assignment-1`\n", "* What to submit for this assignment:\n", " * This Jupyter Notebook (assignment_1.ipynb) should be populated and should be the only change in your pull request.\n", - "* What the pull request link should look like for this assignment: `https://github.com//applying_statistical_concepts/pull/`\n", + "* What the pull request link should look like for this assignment: `https://github.com//LCR/pull/`\n", " * Open a private window in your browser. Copy and paste the link to your pull request into the address bar. Make sure you can see your pull request properly. This helps the technical facilitator and learning support staff review your submission easily.\n", "\n", "Checklist:\n", @@ -365,7 +737,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3.10.4", + "display_name": "dsi", "language": "python", "name": "python3" }, @@ -379,12 +751,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.19" - }, - "vscode": { - "interpreter": { - "hash": "497a84dc8fec8cf8d24e7e87b6d954c9a18a327edc66feb9b9ea7e9e72cc5c7e" - } + "version": "3.9.18" } }, "nbformat": 4,