diff --git a/02_activities/assignments/assignment_1.ipynb b/02_activities/assignments/assignment_1.ipynb index e50cc66eb..fec18a018 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": 99, "id": "4a3485d6-ba58-4660-a983-5680821c5719", "metadata": {}, "outputs": [], @@ -56,10 +56,288 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 100, "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": 100, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "from sklearn.datasets import load_wine\n", "\n", @@ -91,12 +369,69 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 101, "id": "56916892", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 178 entries, 0 to 177\n", + "Data columns (total 14 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 alcohol 178 non-null float64\n", + " 1 malic_acid 178 non-null float64\n", + " 2 ash 178 non-null float64\n", + " 3 alcalinity_of_ash 178 non-null float64\n", + " 4 magnesium 178 non-null float64\n", + " 5 total_phenols 178 non-null float64\n", + " 6 flavanoids 178 non-null float64\n", + " 7 nonflavanoid_phenols 178 non-null float64\n", + " 8 proanthocyanins 178 non-null float64\n", + " 9 color_intensity 178 non-null float64\n", + " 10 hue 178 non-null float64\n", + " 11 od280/od315_of_diluted_wines 178 non-null float64\n", + " 12 proline 178 non-null float64\n", + " 13 class 178 non-null int32 \n", + "dtypes: float64(13), int32(1)\n", + "memory usage: 18.9 KB\n" + ] + } + ], + "source": [ + "# Your answer here\n", + "# Answer: there are 178 rows\n", + "wine_df.info()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ebb25828", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "178\n" + ] + } + ], + "source": [ + "#Getting the number of rows\n", + "print(wine_df.shape[0])" + ] + }, + { + "cell_type": "markdown", + "id": "aa7fd8b9", + "metadata": {}, "source": [ - "# Your answer here" + "There are 178 observations (rows)" ] }, { @@ -109,12 +444,30 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "df0ef103", + "execution_count": 106, + "id": "330e3fcb", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "14\n" + ] + } + ], "source": [ - "# Your answer here" + "# Your answer here\n", + "#Getting the number of columns\n", + "print(wine_df.shape[1])" + ] + }, + { + "cell_type": "markdown", + "id": "4f6f9441", + "metadata": {}, + "source": [ + "There are 14 columns (variables)" ] }, { @@ -127,12 +480,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "id": "47989426", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "int32\n", + "[0 1 2]\n" + ] + } + ], + "source": [ + "# Your answer here\n", + "print(wine_df[\"class\"].dtypes)\n", + "print(wine_df[\"class\"].unique())" + ] + }, + { + "cell_type": "markdown", + "id": "fea1d9d3", + "metadata": {}, "source": [ - "# Your answer here" + "The variable type is integer\n", + "unique values are 0, 1 and 2" ] }, { @@ -146,12 +519,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "id": "bd7b0910", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "13\n" + ] + } + ], "source": [ - "# Your answer here" + "# Your answer here\n", + "print(len(wine_df.columns)-1)" + ] + }, + { + "cell_type": "markdown", + "id": "e97d29f9", + "metadata": {}, + "source": [ + "There are 13 predictor variables" ] }, { @@ -175,13 +565,40 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 49, "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", + "#predictors = wine_df.iloc[:, :-1]\n", "\n", "# Standardize the predictors\n", "scaler = StandardScaler()\n", @@ -204,7 +621,8 @@ "id": "403ef0bb", "metadata": {}, "source": [ - "> Your answer here..." + "> Your answer here...\n", + "It is important to standarize the predictor variables to eliminate the effects of disproportionality between large scale and small scale variables in datasets on prediction outcomes, to improve model performance and to speed up the training model." ] }, { @@ -220,7 +638,9 @@ "id": "fdee5a15", "metadata": {}, "source": [ - "> Your answer here..." + "> Your answer here...\n", + "\n", + "We did elect not to standardize our response variable \"Class\" to keep the originality of the data in \"Class\" column and ensure easy interpretability of the model outcomes in real-world." ] }, { @@ -236,7 +656,10 @@ "id": "f0676c21", "metadata": {}, "source": [ - "> Your answer here..." + "> Your answer here...\n", + "\n", + "Setting a seed is importand because it ensures that the same random values are returned every time the code is run, enabling reproducibility of the results.\n", + "There is no important particular seed value because any seed value can ensure reproducibility of random values. However, for a team working on the same project, same seed value has to be used to get same results because different seed values return arrays of different random values." ] }, { @@ -251,7 +674,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 55, "id": "72c101f2", "metadata": {}, "outputs": [], @@ -261,9 +684,21 @@ "\n", "# split the data into a training and testing set. hint: use train_test_split !\n", "\n", - "# Your code here ..." + "# Your code here ...\n", + "\n", + "predictors_standardized[\"class\"] = wine_df.iloc[:,-1]\n", + "wine_df_train, wine_df_test = train_test_split(\n", + "predictors_standardized, train_size = 0.75, shuffle = True, stratify = predictors_standardized[\"class\"])" ] }, + { + "cell_type": "code", + "execution_count": null, + "id": "97d88636", + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "markdown", "id": "4604ee03", @@ -284,12 +719,508 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 66, "id": "08818c64", "metadata": {}, "outputs": [], "source": [ - "# Your code here..." + "# Your code here...\n", + "#\n", + "knn =KNeighborsClassifier()\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "id": "b15350d6", + "metadata": {}, + "outputs": [], + "source": [ + "parameter_grid = {\n", + " \"n_neighbors\": range(1,50)\n", + "}\n" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "id": "83245e64", + "metadata": {}, + "outputs": [], + "source": [ + "wine_df_tune_grid = GridSearchCV(\n", + " estimator = knn,\n", + " param_grid =parameter_grid,\n", + " cv = 10\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "id": "eed4774f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
GridSearchCV(cv=10, estimator=KNeighborsClassifier(),\n",
+       "             param_grid={'n_neighbors': range(1, 50)})
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" + ], + "text/plain": [ + "GridSearchCV(cv=10, estimator=KNeighborsClassifier(),\n", + " param_grid={'n_neighbors': range(1, 50)})" + ] + }, + "execution_count": 72, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "wine_df_tune_grid.fit(wine_df_train[[\"alcohol\", \"malic_acid\",\"ash\",\"alcalinity_of_ash\",\"magnesium\", \n", + " \"total_phenols\", \"flavanoids\", \"nonflavanoid_phenols\", \"proanthocyanins\", \n", + " \"color_intensity\", \"hue\",\"od280/od315_of_diluted_wines\",\"proline\"]],\n", + " wine_df_train[\"class\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "id": "543dc844", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'n_neighbors': 7}" + ] + }, + "execution_count": 76, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "wine_df_tune_grid.best_params_" ] }, { @@ -308,9 +1239,1257 @@ "execution_count": null, "id": "ffefa9f2", "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
KNeighborsClassifier(n_neighbors=7)
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" + ], + "text/plain": [ + "KNeighborsClassifier(n_neighbors=7)" + ] + }, + "execution_count": 79, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Your code here...\n", + "\n", + "\n", + "knn = KNeighborsClassifier(n_neighbors=7)\n", + "knn" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "id": "1f9ed795", + "metadata": {}, "outputs": [], "source": [ - "# Your code here..." + "\n", + "X =wine_df_train[[\"alcohol\", \"malic_acid\",\"ash\",\n", + " \"alcalinity_of_ash\",\"magnesium\", \n", + " \"total_phenols\", \"flavanoids\", \n", + " \"nonflavanoid_phenols\", \"proanthocyanins\", \n", + " \"color_intensity\", \"hue\",\"od280/od315_of_diluted_wines\",\"proline\"]]\n", + "y= wine_df_train[\"class\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "id": "66a5e6e2", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
KNeighborsClassifier(n_neighbors=7)
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" + ], + "text/plain": [ + "KNeighborsClassifier(n_neighbors=7)" + ] + }, + "execution_count": 81, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "knn.fit(X,y)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c60148f3", + "metadata": {}, + "outputs": [], + "source": [ + "wine_df_test[\"predicted_class\"] =knn.predict(\n", + " wine_df_test[[\"alcohol\", \"malic_acid\",\"ash\",\n", + " \"alcalinity_of_ash\",\"magnesium\", \n", + " \"total_phenols\", \"flavanoids\", \n", + " \"nonflavanoid_phenols\", \"proanthocyanins\", \n", + " \"color_intensity\", \"hue\",\"od280/od315_of_diluted_wines\",\"proline\"]])" + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "id": "56221b6e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " class predicted_class\n", + "102 1 1\n", + "84 1 1\n", + "96 1 2\n", + "65 1 0\n", + "79 1 1\n", + "17 0 0\n", + "109 1 1\n", + "113 1 1\n", + "28 0 0\n", + "159 2 2\n", + "38 0 0\n", + "34 0 0\n", + "125 1 1\n", + "115 1 1\n", + "71 1 1\n", + "76 1 1\n", + "131 2 2\n", + "33 0 0\n", + "60 1 1\n", + "19 0 0\n", + "114 1 1\n", + "47 0 0\n", + "48 0 0\n", + "158 2 2\n", + "133 2 2\n", + "137 2 2\n", + "154 2 2\n", + "136 2 2\n", + "2 0 0\n", + "168 2 2\n", + "117 1 1\n", + "32 0 0\n", + "22 0 0\n", + "108 1 1\n", + "73 1 0\n", + "77 1 1\n", + "142 2 2\n", + "9 0 0\n", + "85 1 1\n", + "58 0 0\n", + "45 0 0\n", + "175 2 2\n", + "42 0 0\n", + "143 2 2\n", + "177 2 2" + ] + }, + "execution_count": 86, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "wine_df_test[[\"class\", \"predicted_class\"]]" + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "id": "20ab39f6", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.9333333333333333" + ] + }, + "execution_count": 89, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "#Using accuracy score\n", + "knn.score(wine_df_test[[\"alcohol\", \"malic_acid\",\"ash\",\"alcalinity_of_ash\",\"magnesium\", \n", + " \"total_phenols\", \"flavanoids\", \"nonflavanoid_phenols\", \"proanthocyanins\", \n", + " \"color_intensity\", \"hue\",\"od280/od315_of_diluted_wines\",\"proline\"]],\n", + " wine_df_test[\"class\"])" ] }, { @@ -365,7 +2544,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3.10.4", + "display_name": "dsi_participant", "language": "python", "name": "python3" }, @@ -380,11 +2559,6 @@ "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.19" - }, - "vscode": { - "interpreter": { - "hash": "497a84dc8fec8cf8d24e7e87b6d954c9a18a327edc66feb9b9ea7e9e72cc5c7e" - } } }, "nbformat": 4,