diff --git a/your-code/main.ipynb b/your-code/main.ipynb index f6f69ed..1589863 100755 --- a/your-code/main.ipynb +++ b/your-code/main.ipynb @@ -12,12 +12,14 @@ }, { "cell_type": "code", - "execution_count": 174, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# Import your libraries:\n", - "\n" + "import numpy as np\n", + "import scipy\n", + "from sklearn import datasets" ] }, { @@ -38,12 +40,92 @@ }, { "cell_type": "code", - "execution_count": 175, - "metadata": {}, - "outputs": [], + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'data': array([[ 0.03807591, 0.05068012, 0.06169621, ..., -0.00259226,\n", + " 0.01990842, -0.01764613],\n", + " [-0.00188202, -0.04464164, -0.05147406, ..., -0.03949338,\n", + " -0.06832974, -0.09220405],\n", + " [ 0.08529891, 0.05068012, 0.04445121, ..., -0.00259226,\n", + " 0.00286377, -0.02593034],\n", + " ...,\n", + " [ 0.04170844, 0.05068012, -0.01590626, ..., -0.01107952,\n", + " -0.04687948, 0.01549073],\n", + " [-0.04547248, -0.04464164, 0.03906215, ..., 0.02655962,\n", + " 0.04452837, -0.02593034],\n", + " [-0.04547248, -0.04464164, -0.0730303 , ..., -0.03949338,\n", + " -0.00421986, 0.00306441]]),\n", + " 'target': array([151., 75., 141., 206., 135., 97., 138., 63., 110., 310., 101.,\n", + " 69., 179., 185., 118., 171., 166., 144., 97., 168., 68., 49.,\n", + " 68., 245., 184., 202., 137., 85., 131., 283., 129., 59., 341.,\n", + " 87., 65., 102., 265., 276., 252., 90., 100., 55., 61., 92.,\n", + " 259., 53., 190., 142., 75., 142., 155., 225., 59., 104., 182.,\n", + " 128., 52., 37., 170., 170., 61., 144., 52., 128., 71., 163.,\n", + " 150., 97., 160., 178., 48., 270., 202., 111., 85., 42., 170.,\n", + " 200., 252., 113., 143., 51., 52., 210., 65., 141., 55., 134.,\n", + " 42., 111., 98., 164., 48., 96., 90., 162., 150., 279., 92.,\n", + " 83., 128., 102., 302., 198., 95., 53., 134., 144., 232., 81.,\n", + " 104., 59., 246., 297., 258., 229., 275., 281., 179., 200., 200.,\n", + " 173., 180., 84., 121., 161., 99., 109., 115., 268., 274., 158.,\n", + " 107., 83., 103., 272., 85., 280., 336., 281., 118., 317., 235.,\n", + " 60., 174., 259., 178., 128., 96., 126., 288., 88., 292., 71.,\n", + " 197., 186., 25., 84., 96., 195., 53., 217., 172., 131., 214.,\n", + " 59., 70., 220., 268., 152., 47., 74., 295., 101., 151., 127.,\n", + " 237., 225., 81., 151., 107., 64., 138., 185., 265., 101., 137.,\n", + " 143., 141., 79., 292., 178., 91., 116., 86., 122., 72., 129.,\n", + " 142., 90., 158., 39., 196., 222., 277., 99., 196., 202., 155.,\n", + " 77., 191., 70., 73., 49., 65., 263., 248., 296., 214., 185.,\n", + " 78., 93., 252., 150., 77., 208., 77., 108., 160., 53., 220.,\n", + " 154., 259., 90., 246., 124., 67., 72., 257., 262., 275., 177.,\n", + " 71., 47., 187., 125., 78., 51., 258., 215., 303., 243., 91.,\n", + " 150., 310., 153., 346., 63., 89., 50., 39., 103., 308., 116.,\n", + " 145., 74., 45., 115., 264., 87., 202., 127., 182., 241., 66.,\n", + " 94., 283., 64., 102., 200., 265., 94., 230., 181., 156., 233.,\n", + " 60., 219., 80., 68., 332., 248., 84., 200., 55., 85., 89.,\n", + " 31., 129., 83., 275., 65., 198., 236., 253., 124., 44., 172.,\n", + " 114., 142., 109., 180., 144., 163., 147., 97., 220., 190., 109.,\n", + " 191., 122., 230., 242., 248., 249., 192., 131., 237., 78., 135.,\n", + " 244., 199., 270., 164., 72., 96., 306., 91., 214., 95., 216.,\n", + " 263., 178., 113., 200., 139., 139., 88., 148., 88., 243., 71.,\n", + " 77., 109., 272., 60., 54., 221., 90., 311., 281., 182., 321.,\n", + " 58., 262., 206., 233., 242., 123., 167., 63., 197., 71., 168.,\n", + " 140., 217., 121., 235., 245., 40., 52., 104., 132., 88., 69.,\n", + " 219., 72., 201., 110., 51., 277., 63., 118., 69., 273., 258.,\n", + " 43., 198., 242., 232., 175., 93., 168., 275., 293., 281., 72.,\n", + " 140., 189., 181., 209., 136., 261., 113., 131., 174., 257., 55.,\n", + " 84., 42., 146., 212., 233., 91., 111., 152., 120., 67., 310.,\n", + " 94., 183., 66., 173., 72., 49., 64., 48., 178., 104., 132.,\n", + " 220., 57.]),\n", + " 'frame': None,\n", + " 'DESCR': '.. _diabetes_dataset:\\n\\nDiabetes dataset\\n----------------\\n\\nTen baseline variables, age, sex, body mass index, average blood\\npressure, and six blood serum measurements were obtained for each of n =\\n442 diabetes patients, as well as the response of interest, a\\nquantitative measure of disease progression one year after baseline.\\n\\n**Data Set Characteristics:**\\n\\n :Number of Instances: 442\\n\\n :Number of Attributes: First 10 columns are numeric predictive values\\n\\n :Target: Column 11 is a quantitative measure of disease progression one year after baseline\\n\\n :Attribute Information:\\n - age age in years\\n - sex\\n - bmi body mass index\\n - bp average blood pressure\\n - s1 tc, T-Cells (a type of white blood cells)\\n - s2 ldl, low-density lipoproteins\\n - s3 hdl, high-density lipoproteins\\n - s4 tch, thyroid stimulating hormone\\n - s5 ltg, lamotrigine\\n - s6 glu, blood sugar level\\n\\nNote: Each of these 10 feature variables have been mean centered and scaled by the standard deviation times `n_samples` (i.e. the sum of squares of each column totals 1).\\n\\nSource URL:\\nhttps://www4.stat.ncsu.edu/~boos/var.select/diabetes.html\\n\\nFor more information see:\\nBradley Efron, Trevor Hastie, Iain Johnstone and Robert Tibshirani (2004) \"Least Angle Regression,\" Annals of Statistics (with discussion), 407-499.\\n(https://web.stanford.edu/~hastie/Papers/LARS/LeastAngle_2002.pdf)',\n", + " 'feature_names': ['age',\n", + " 'sex',\n", + " 'bmi',\n", + " 'bp',\n", + " 's1',\n", + " 's2',\n", + " 's3',\n", + " 's4',\n", + " 's5',\n", + " 's6'],\n", + " 'data_filename': '/home/vdiazpliego/anaconda3/lib/python3.8/site-packages/sklearn/datasets/data/diabetes_data.csv.gz',\n", + " 'target_filename': '/home/vdiazpliego/anaconda3/lib/python3.8/site-packages/sklearn/datasets/data/diabetes_target.csv.gz'}" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Your code here:\n", - "\n" + "\n", + "diabetes = datasets.load_diabetes()\n", + "diabetes" ] }, { @@ -55,12 +137,23 @@ }, { "cell_type": "code", - "execution_count": 176, - "metadata": {}, - "outputs": [], + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "dict_keys(['data', 'target', 'frame', 'DESCR', 'feature_names', 'data_filename', 'target_filename'])" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Your code here:\n", - "\n" + "diabetes.keys()\n" ] }, { @@ -72,11 +165,23 @@ }, { "cell_type": "code", - "execution_count": 177, - "metadata": {}, - "outputs": [], + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'.. _diabetes_dataset:\\n\\nDiabetes dataset\\n----------------\\n\\nTen baseline variables, age, sex, body mass index, average blood\\npressure, and six blood serum measurements were obtained for each of n =\\n442 diabetes patients, as well as the response of interest, a\\nquantitative measure of disease progression one year after baseline.\\n\\n**Data Set Characteristics:**\\n\\n :Number of Instances: 442\\n\\n :Number of Attributes: First 10 columns are numeric predictive values\\n\\n :Target: Column 11 is a quantitative measure of disease progression one year after baseline\\n\\n :Attribute Information:\\n - age age in years\\n - sex\\n - bmi body mass index\\n - bp average blood pressure\\n - s1 tc, T-Cells (a type of white blood cells)\\n - s2 ldl, low-density lipoproteins\\n - s3 hdl, high-density lipoproteins\\n - s4 tch, thyroid stimulating hormone\\n - s5 ltg, lamotrigine\\n - s6 glu, blood sugar level\\n\\nNote: Each of these 10 feature variables have been mean centered and scaled by the standard deviation times `n_samples` (i.e. the sum of squares of each column totals 1).\\n\\nSource URL:\\nhttps://www4.stat.ncsu.edu/~boos/var.select/diabetes.html\\n\\nFor more information see:\\nBradley Efron, Trevor Hastie, Iain Johnstone and Robert Tibshirani (2004) \"Least Angle Regression,\" Annals of Statistics (with discussion), 407-499.\\n(https://web.stanford.edu/~hastie/Papers/LARS/LeastAngle_2002.pdf)'" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# Your code here:\n" + "# Your code here:\n", + "diabetes.DESCR" ] }, { @@ -104,12 +209,232 @@ }, { "cell_type": "code", - "execution_count": 178, - "metadata": {}, - "outputs": [], + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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mpgcylindersdisplacementhorse_powerweightaccelerationmodel_yearcar_name
018.08307.0130.0350412.070\\t\"chevrolet chevelle malibu\"
115.08350.0165.0369311.570\\t\"buick skylark 320\"
218.08318.0150.0343611.070\\t\"plymouth satellite\"
316.08304.0150.0343312.070\\t\"amc rebel sst\"
417.08302.0140.0344910.570\\t\"ford torino\"
\n", + "
" + ], + "text/plain": [ + " mpg cylinders displacement horse_power weight acceleration \\\n", + "0 18.0 8 307.0 130.0 3504 12.0 \n", + "1 15.0 8 350.0 165.0 3693 11.5 \n", + "2 18.0 8 318.0 150.0 3436 11.0 \n", + "3 16.0 8 304.0 150.0 3433 12.0 \n", + "4 17.0 8 302.0 140.0 3449 10.5 \n", + "\n", + " model_year car_name \n", + "0 70 \\t\"chevrolet chevelle malibu\" \n", + "1 70 \\t\"buick skylark 320\" \n", + "2 70 \\t\"plymouth satellite\" \n", + "3 70 \\t\"amc rebel sst\" \n", + "4 70 \\t\"ford torino\" " + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Your code here:\n", - "\n" + "auto.head()\n" ] }, { @@ -253,12 +728,35 @@ }, { "cell_type": "code", - "execution_count": 185, - "metadata": {}, - "outputs": [], + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 398 entries, 0 to 397\n", + "Data columns (total 8 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 mpg 398 non-null float64\n", + " 1 cylinders 398 non-null int64 \n", + " 2 displacement 398 non-null float64\n", + " 3 horse_power 392 non-null float64\n", + " 4 weight 398 non-null int64 \n", + " 5 acceleration 398 non-null float64\n", + " 6 model_year 398 non-null int64 \n", + " 7 car_name 398 non-null object \n", + "dtypes: float64(4), int64(3), object(1)\n", + "memory usage: 25.0+ KB\n" + ] + } + ], "source": [ "# Your code here:\n", - "\n" + "\n", + "auto.info()" ] }, { @@ -270,12 +768,32 @@ }, { "cell_type": "code", - "execution_count": 186, - "metadata": {}, - "outputs": [], + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "count 398.000000\n", + "mean 76.010050\n", + "std 3.697627\n", + "min 70.000000\n", + "25% 73.000000\n", + "50% 76.000000\n", + "75% 79.000000\n", + "max 82.000000\n", + "Name: model_year, dtype: float64" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Your code here:\n", - "\n" + "auto.model_year.describe()\n", + "#el mas moderno del año 82, y el más antiguo del año 70" ] }, { @@ -287,12 +805,38 @@ }, { "cell_type": "code", - "execution_count": 187, - "metadata": {}, - "outputs": [], + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Int64Index: 392 entries, 0 to 397\n", + "Data columns (total 8 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 mpg 392 non-null float64\n", + " 1 cylinders 392 non-null int64 \n", + " 2 displacement 392 non-null float64\n", + " 3 horse_power 392 non-null float64\n", + " 4 weight 392 non-null int64 \n", + " 5 acceleration 392 non-null float64\n", + " 6 model_year 392 non-null int64 \n", + " 7 car_name 392 non-null object \n", + "dtypes: float64(4), int64(3), object(1)\n", + "memory usage: 27.6+ KB\n" + ] + } + ], "source": [ "# Your code here:\n", - "\n" + "nan_cols = auto.isna().sum()\n", + "nan_cols[nan_cols>0]\n", + "\n", + "auto.dropna(inplace=True)\n", + "auto.info()\n" ] }, { @@ -304,12 +848,30 @@ }, { "cell_type": "code", - "execution_count": 188, - "metadata": {}, - "outputs": [], + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "4 199\n", + "8 103\n", + "6 83\n", + "3 4\n", + "5 3\n", + "Name: cylinders, dtype: int64" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Your code here:\n", - "\n" + "\n", + "auto.cylinders.value_counts()\n", + "#exiten 5 posibles valores de cilindros. (4,8,6,3,5)" ] }, { @@ -323,31 +885,36 @@ }, { "cell_type": "code", - "execution_count": 189, + "execution_count": 30, "metadata": {}, "outputs": [], "source": [ - "# Import the necessary function\n", - "\n" + "X=auto.drop(columns=['mpg'])._get_numeric_data()\n", + "\n", + "y=auto.mpg" ] }, { "cell_type": "code", - "execution_count": 190, + "execution_count": 32, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "((313, 6), (313,), (79, 6), (79,))" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# Your code here:\n", - "\n" + "X_train, X_test, y_train, y_test = tts(X, y, test_size=0.2, train_size=0.8, random_state=42)\n", + "X_train.shape, y_train.shape, X_test.shape, y_test.shape\n" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, { "cell_type": "markdown", "metadata": {}, @@ -357,12 +924,33 @@ }, { "cell_type": "code", - "execution_count": 191, - "metadata": {}, - "outputs": [], + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "mpg True\n", + "cylinders True\n", + "displacement True\n", + "horse_power True\n", + "weight True\n", + "acceleration True\n", + "model_year True\n", + "dtype: bool" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Your code here:\n", - "\n" + "from sklearn.linear_model import LinearRegression as LinReg\n", + "\n", + "from sklearn.metrics import r2_score\n", + "np.isfinite(auto.all())\n" ] }, { @@ -374,14 +962,66 @@ }, { "cell_type": "code", - "execution_count": 192, + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Your code here:\n", + "X_train.isnull().count(),\n", + "y_train.isnull().sum()\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 38, "metadata": {}, "outputs": [], "source": [ - "# Your code here:\n", + "X_train.horse_power.fillna(X_train.horse_power.mean(), inplace= True)\n", + "X_test.horse_power.fillna(X_test.horse_power.mean(), inplace= True)\n", "\n" ] }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "LinearRegression()" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "linreg=LinReg()\n", + "linreg.fit(X_train, y_train)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "markdown", "metadata": {}, @@ -395,21 +1035,35 @@ }, { "cell_type": "code", - "execution_count": 193, + "execution_count": 27, "metadata": {}, "outputs": [], "source": [ "# Import the necessary function:\n", - "\n" + "\n", + "from sklearn.metrics import r2_score\n" ] }, { "cell_type": "code", - "execution_count": 194, - "metadata": {}, - "outputs": [], + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.8037468831261813" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Your code here:\n", + "y_pred=linreg.predict(X_test)\n", + "r2_score(y_pred, y_test)\n", "\n" ] }, @@ -424,11 +1078,25 @@ }, { "cell_type": "code", - "execution_count": 195, - "metadata": {}, - "outputs": [], + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.8037468831261813" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Your code here:\n", + "y_test_pred=linreg.predict(X_test)\n", + "\n", + "r2_score(y_test_pred, y_test)\n", "\n" ] }, @@ -445,12 +1113,13 @@ }, { "cell_type": "code", - "execution_count": 196, + "execution_count": 44, "metadata": {}, "outputs": [], "source": [ "# Your code here:\n", - "\n" + "\n", + "X_train09, X_test09, y_train09, y_test09 = tts(X, y, test_size=0.1, train_size=0.9, random_state=42)\n" ] }, { @@ -462,14 +1131,38 @@ }, { "cell_type": "code", - "execution_count": 197, + "execution_count": 46, "metadata": {}, "outputs": [], "source": [ "# Your code here:\n", + "X_train09.horse_power.fillna(X_train09.horse_power.mean(), inplace= True)\n", "\n" ] }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "LinearRegression()" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "auto_model09=LinReg()\n", + "\n", + "\n", + "linreg.fit(X_train09, y_train09)" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -479,12 +1172,13 @@ }, { "cell_type": "code", - "execution_count": 198, + "execution_count": 48, "metadata": {}, "outputs": [], "source": [ "# Your code here:\n", - "\n" + "\n", + "y_pred09=linreg.predict(X_test09)\n" ] }, { @@ -496,12 +1190,33 @@ }, { "cell_type": "code", - "execution_count": 199, + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.8363926533372651" + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Your code here:\n", + "\n", + "r2_score(y_pred09, y_test09)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 50, "metadata": {}, "outputs": [], "source": [ - "# Your code here:\n", - "\n" + "#Ha mejorado r2 con este modelo un 3% aprox" ] }, { @@ -531,7 +1246,7 @@ }, { "cell_type": "code", - "execution_count": 200, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -548,7 +1263,7 @@ }, { "cell_type": "code", - "execution_count": 201, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -567,7 +1282,7 @@ }, { "cell_type": "code", - "execution_count": 202, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -584,7 +1299,7 @@ }, { "cell_type": "code", - "execution_count": 203, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -616,7 +1331,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.6" + "version": "3.8.5" } }, "nbformat": 4,