From 106099f2fd8a83d5fffaa944139cae69b82d0d28 Mon Sep 17 00:00:00 2001 From: Nader Mostaghimi Date: Fri, 12 Dec 2025 22:00:22 -0800 Subject: [PATCH] Updated assignment-1 by Nader Mostaghimi --- 02_activities/assignments/assignment_1.ipynb | 903 ++++++++++++++++++- 1 file changed, 872 insertions(+), 31 deletions(-) diff --git a/02_activities/assignments/assignment_1.ipynb b/02_activities/assignments/assignment_1.ipynb index 28d4df017..9144ae9e2 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": 70, "id": "4a3485d6-ba58-4660-a983-5680821c5719", "metadata": {}, "outputs": [], @@ -56,10 +56,288 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 71, "id": "a431d282-f9ca-4d5d-8912-71ffc9d8ea19", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
alcoholmalic_acidashalcalinity_of_ashmagnesiumtotal_phenolsflavanoidsnonflavanoid_phenolsproanthocyaninscolor_intensityhueod280/od315_of_diluted_winesprolineclass
014.231.712.4315.6127.02.803.060.282.295.641.043.921065.00
113.201.782.1411.2100.02.652.760.261.284.381.053.401050.00
213.162.362.6718.6101.02.803.240.302.815.681.033.171185.00
314.371.952.5016.8113.03.853.490.242.187.800.863.451480.00
413.242.592.8721.0118.02.802.690.391.824.321.042.93735.00
.............................................
17313.715.652.4520.595.01.680.610.521.067.700.641.74740.02
17413.403.912.4823.0102.01.800.750.431.417.300.701.56750.02
17513.274.282.2620.0120.01.590.690.431.3510.200.591.56835.02
17613.172.592.3720.0120.01.650.680.531.469.300.601.62840.02
17714.134.102.7424.596.02.050.760.561.359.200.611.60560.02
\n", + "

178 rows × 14 columns

\n", + "
" + ], + "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": 71, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "from sklearn.datasets import load_wine\n", "\n", @@ -91,12 +369,13 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 72, "id": "56916892", "metadata": {}, "outputs": [], "source": [ - "# Your answer here" + "# Your answer here\n", + "# 178 observations" ] }, { @@ -109,12 +388,13 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 73, "id": "df0ef103", "metadata": {}, "outputs": [], "source": [ - "# Your answer here" + "# Your answer here\n", + "# 14 variables" ] }, { @@ -127,12 +407,47 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 74, "id": "47989426", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "dtype('int64')" + ] + }, + "execution_count": 74, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Your answer here\n", + "wine_df['class'].dtype\n", + "# int64 is the variable type" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "id": "682e1b8a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0, 1, 2])" + ] + }, + "execution_count": 75, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# Your answer here" + "wine_df[\"class\"].unique()\n", + "# the unique values are 0, 1, and 2" ] }, { @@ -146,12 +461,13 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 76, "id": "bd7b0910", "metadata": {}, "outputs": [], "source": [ - "# Your answer here" + "# Your answer here\n", + "# 13 predictor variables" ] }, { @@ -175,10 +491,37 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 77, "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", @@ -187,10 +530,237 @@ "scaler = StandardScaler()\n", "predictors_standardized = pd.DataFrame(scaler.fit_transform(predictors), columns=predictors.columns)\n", "\n", + "\n", "# Display the head of the standardized predictors\n", "print(predictors_standardized.head())" ] }, + { + "cell_type": "code", + "execution_count": 78, + "id": "c5a50c3c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
alcoholmalic_acidashalcalinity_of_ashmagnesiumtotal_phenolsflavanoidsnonflavanoid_phenolsproanthocyaninscolor_intensityhueod280/od315_of_diluted_winesproline
count1.780000e+021.780000e+021.780000e+021.780000e+021.780000e+021.780000e+021.780000e+021.780000e+021.780000e+021.780000e+021.780000e+021.780000e+021.780000e+02
mean7.943708e-153.592632e-16-4.066660e-15-7.983626e-17-7.983626e-17-3.991813e-179.979533e-16-5.588538e-16-1.656602e-15-3.442939e-161.636643e-152.235415e-15-1.197544e-16
std1.002821e+001.002821e+001.002821e+001.002821e+001.002821e+001.002821e+001.002821e+001.002821e+001.002821e+001.002821e+001.002821e+001.002821e+001.002821e+00
min-2.434235e+00-1.432983e+00-3.679162e+00-2.671018e+00-2.088255e+00-2.107246e+00-1.695971e+00-1.868234e+00-2.069034e+00-1.634288e+00-2.094732e+00-1.895054e+00-1.493188e+00
25%-7.882448e-01-6.587486e-01-5.721225e-01-6.891372e-01-8.244151e-01-8.854682e-01-8.275393e-01-7.401412e-01-5.972835e-01-7.951025e-01-7.675624e-01-9.522483e-01-7.846378e-01
50%6.099988e-02-4.231120e-01-2.382132e-021.518295e-03-1.222817e-019.595986e-021.061497e-01-1.760948e-01-6.289785e-02-1.592246e-013.312687e-022.377348e-01-2.337204e-01
75%8.361286e-016.697929e-016.981085e-016.020883e-015.096384e-018.089974e-018.490851e-016.095413e-016.291754e-014.939560e-017.131644e-017.885875e-017.582494e-01
max2.259772e+003.109192e+003.156325e+003.154511e+004.371372e+002.539515e+003.062832e+002.402403e+003.485073e+003.435432e+003.301694e+001.960915e+002.971473e+00
\n", + "
" + ], + "text/plain": [ + " alcohol malic_acid ash alcalinity_of_ash \\\n", + "count 1.780000e+02 1.780000e+02 1.780000e+02 1.780000e+02 \n", + "mean 7.943708e-15 3.592632e-16 -4.066660e-15 -7.983626e-17 \n", + "std 1.002821e+00 1.002821e+00 1.002821e+00 1.002821e+00 \n", + "min -2.434235e+00 -1.432983e+00 -3.679162e+00 -2.671018e+00 \n", + "25% -7.882448e-01 -6.587486e-01 -5.721225e-01 -6.891372e-01 \n", + "50% 6.099988e-02 -4.231120e-01 -2.382132e-02 1.518295e-03 \n", + "75% 8.361286e-01 6.697929e-01 6.981085e-01 6.020883e-01 \n", + "max 2.259772e+00 3.109192e+00 3.156325e+00 3.154511e+00 \n", + "\n", + " magnesium total_phenols flavanoids nonflavanoid_phenols \\\n", + "count 1.780000e+02 1.780000e+02 1.780000e+02 1.780000e+02 \n", + "mean -7.983626e-17 -3.991813e-17 9.979533e-16 -5.588538e-16 \n", + "std 1.002821e+00 1.002821e+00 1.002821e+00 1.002821e+00 \n", + "min -2.088255e+00 -2.107246e+00 -1.695971e+00 -1.868234e+00 \n", + "25% -8.244151e-01 -8.854682e-01 -8.275393e-01 -7.401412e-01 \n", + "50% -1.222817e-01 9.595986e-02 1.061497e-01 -1.760948e-01 \n", + "75% 5.096384e-01 8.089974e-01 8.490851e-01 6.095413e-01 \n", + "max 4.371372e+00 2.539515e+00 3.062832e+00 2.402403e+00 \n", + "\n", + " proanthocyanins color_intensity hue \\\n", + "count 1.780000e+02 1.780000e+02 1.780000e+02 \n", + "mean -1.656602e-15 -3.442939e-16 1.636643e-15 \n", + "std 1.002821e+00 1.002821e+00 1.002821e+00 \n", + "min -2.069034e+00 -1.634288e+00 -2.094732e+00 \n", + "25% -5.972835e-01 -7.951025e-01 -7.675624e-01 \n", + "50% -6.289785e-02 -1.592246e-01 3.312687e-02 \n", + "75% 6.291754e-01 4.939560e-01 7.131644e-01 \n", + "max 3.485073e+00 3.435432e+00 3.301694e+00 \n", + "\n", + " od280/od315_of_diluted_wines proline \n", + "count 1.780000e+02 1.780000e+02 \n", + "mean 2.235415e-15 -1.197544e-16 \n", + "std 1.002821e+00 1.002821e+00 \n", + "min -1.895054e+00 -1.493188e+00 \n", + "25% -9.522483e-01 -7.846378e-01 \n", + "50% 2.377348e-01 -2.337204e-01 \n", + "75% 7.885875e-01 7.582494e-01 \n", + "max 1.960915e+00 2.971473e+00 " + ] + }, + "execution_count": 78, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "predictors_standardized.describe()" + ] + }, { "cell_type": "markdown", "id": "9981ca48", @@ -204,7 +774,7 @@ "id": "403ef0bb", "metadata": {}, "source": [ - "> Your answer here..." + "> Your answer here: so that your variables are not weighted differently and are balanced. Final results will be more reliable. Without standardization, models will be dominated by varaibles with higher scales." ] }, { @@ -220,7 +790,7 @@ "id": "fdee5a15", "metadata": {}, "source": [ - "> Your answer here..." + "> Your answer here: That is because we intend to determine the Class variable of our wine." ] }, { @@ -236,7 +806,7 @@ "id": "f0676c21", "metadata": {}, "source": [ - "> Your answer here..." + "> Your answer here: It is important to set a seed to randomize our function the same way each time - so that it is reprodicible. No, the particular seed value is not important since using the same value is what is important to remain consistent in our tests and training." ] }, { @@ -251,7 +821,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 79, "id": "72c101f2", "metadata": {}, "outputs": [], @@ -261,7 +831,18 @@ "\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 = predictors_standardized \n", + "y = wine_df['class']\n", + "\n", + "X_train, X_test, y_train, y_test = train_test_split(\n", + " X,\n", + " y,\n", + " train_size= 0.75, \n", + " shuffle= True, \n", + " stratify = y,\n", + " random_state = 123\n", + " )" ] }, { @@ -284,12 +865,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 80, "id": "08818c64", "metadata": {}, "outputs": [], "source": [ - "# Your code here..." + "# Your code here...\n", + "\n", + "knn = KNeighborsClassifier(n_neighbors = 50)\n", + "\n", + "parameter_grid = {\n", + " \"n_neighbors\" : range(1, 51)\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "id": "0417a55a", + "metadata": {}, + "outputs": [], + "source": [ + "wine_tune_grid = GridSearchCV(\n", + " estimator = knn,\n", + " param_grid = parameter_grid,\n", + " cv = 10\n", + ")" ] }, { @@ -305,12 +906,257 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 82, + "id": "63650cd6", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(pandas.core.frame.DataFrame, pandas.core.series.Series)" + ] + }, + "execution_count": 82, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "type(X_train), type(y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "id": "8100cdb7", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "((133, 13), (133,))" + ] + }, + "execution_count": 83, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X_train.shape, y_train.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 84, + "id": "ac8eeb30", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
alcoholmalic_acidashalcalinity_of_ashmagnesiumtotal_phenolsflavanoidsnonflavanoid_phenolsproanthocyaninscolor_intensityhueod280/od315_of_diluted_winesproline
78-0.828391-1.208567-1.522511-1.4098212.545825-0.633101-0.179981-0.0955172.048364-0.7172400.449924-0.4261130.009893
01.518613-0.5622500.232053-1.1695931.9139050.8089971.034819-0.6595631.2248840.2517170.3621771.8479201.013009
150.777454-0.4724831.218995-0.6891370.8607050.8891140.884224-0.498407-0.2293460.9697831.4151390.3789791.793210
132.160950-0.5442970.085839-2.430790-0.6137751.2896971.6673180.5491082.1359680.1479001.2835180.1671131.283691
141.703902-0.4186240.049285-2.2506190.1585721.6101631.617120-0.5789852.3987801.0562971.0641510.5484722.547935
\n", + "
" + ], + "text/plain": [ + " alcohol malic_acid ash alcalinity_of_ash magnesium \\\n", + "78 -0.828391 -1.208567 -1.522511 -1.409821 2.545825 \n", + "0 1.518613 -0.562250 0.232053 -1.169593 1.913905 \n", + "15 0.777454 -0.472483 1.218995 -0.689137 0.860705 \n", + "13 2.160950 -0.544297 0.085839 -2.430790 -0.613775 \n", + "14 1.703902 -0.418624 0.049285 -2.250619 0.158572 \n", + "\n", + " total_phenols flavanoids nonflavanoid_phenols proanthocyanins \\\n", + "78 -0.633101 -0.179981 -0.095517 2.048364 \n", + "0 0.808997 1.034819 -0.659563 1.224884 \n", + "15 0.889114 0.884224 -0.498407 -0.229346 \n", + "13 1.289697 1.667318 0.549108 2.135968 \n", + "14 1.610163 1.617120 -0.578985 2.398780 \n", + "\n", + " color_intensity hue od280/od315_of_diluted_wines proline \n", + "78 -0.717240 0.449924 -0.426113 0.009893 \n", + "0 0.251717 0.362177 1.847920 1.013009 \n", + "15 0.969783 1.415139 0.378979 1.793210 \n", + "13 0.147900 1.283518 0.167113 1.283691 \n", + "14 1.056297 1.064151 0.548472 2.547935 " + ] + }, + "execution_count": 84, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X_train.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "id": "0766251e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "78 1\n", + "0 0\n", + "15 0\n", + "13 0\n", + "14 0\n", + "Name: class, dtype: int64" + ] + }, + "execution_count": 85, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "y_train.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 87, "id": "ffefa9f2", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "0.9333333333333333" + ] + }, + "execution_count": 87, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# Your code here..." + "# Your code here...\n", + "wine_tune_grid.fit(X_train, y_train)\n", + "\n", + "y_pred = wine_tune_grid.best_estimator_.predict(X_test)\n", + "\n", + "accuracy = accuracy_score(y_test, y_pred)\n", + "\n", + "accuracy" ] }, { @@ -365,7 +1211,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3.10.4", + "display_name": "lcr-env (3.11.14)", "language": "python", "name": "python3" }, @@ -379,12 +1225,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.19" - }, - "vscode": { - "interpreter": { - "hash": "497a84dc8fec8cf8d24e7e87b6d954c9a18a327edc66feb9b9ea7e9e72cc5c7e" - } + "version": "3.11.14" } }, "nbformat": 4,