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a3bd83b
Update Diabetes Ridge Regression Training.ipynb
GabbyHills Aug 4, 2023
9947719
Merge pull request #1 from GabbyHills/GabbyHills-patch-1-1
GabbyHills Aug 4, 2023
c3b6e0a
Update Diabetes Ridge Regression Training.ipynb
GabbyHills Aug 5, 2023
f2ff0b6
Update Diabetes Ridge Regression Training.ipynb
GabbyHills Aug 5, 2023
465393c
Update Diabetes Ridge Regression Training.ipynb
GabbyHills Aug 5, 2023
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Update Diabetes Ridge Regression Training.ipynb
GabbyHills Aug 5, 2023
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Update Diabetes Ridge Regression Training.ipynb
GabbyHills Aug 5, 2023
380d272
Update Diabetes Ridge Regression Training.ipynb
GabbyHills Aug 5, 2023
e5b3d21
Merge pull request #2 from GabbyHills/GabbyHills-patch-1-1
GabbyHills Aug 5, 2023
f3aec3c
Update Diabetes Ridge Regression Training.ipynb
GabbyHills Aug 5, 2023
77c01c2
Update Diabetes Ridge Regression Training.ipynb
GabbyHills Aug 5, 2023
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Update Diabetes Ridge Regression Training.ipynb
GabbyHills Aug 5, 2023
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Update Diabetes Ridge Regression Training.ipynb
GabbyHills Aug 5, 2023
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Update Diabetes Ridge Regression Training.ipynb
GabbyHills Aug 5, 2023
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Update Diabetes Ridge Regression Training.ipynb
GabbyHills Aug 5, 2023
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Update Diabetes Ridge Regression Training.ipynb
GabbyHills Aug 5, 2023
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Update Diabetes Ridge Regression Training.ipynb
GabbyHills Aug 5, 2023
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Update Diabetes Ridge Regression Training.ipynb
GabbyHills Aug 5, 2023
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Update Diabetes Ridge Regression Training.ipynb
GabbyHills Aug 5, 2023
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Update Diabetes Ridge Regression Training.ipynb
GabbyHills Aug 5, 2023
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Update Diabetes Ridge Regression Training.ipynb
GabbyHills Aug 5, 2023
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Update Diabetes Ridge Regression Training.ipynb
GabbyHills Aug 5, 2023
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Update Diabetes Ridge Regression Training.ipynb
GabbyHills Aug 5, 2023
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Merge pull request #3 from GabbyHills/GabbyHills-patch-2
GabbyHills Aug 5, 2023
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Update Diabetes Ridge Regression Training.ipynb
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Merge pull request #4 from GabbyHills/GabbyHills-patch-3
GabbyHills Aug 5, 2023
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Update Diabetes Ridge Regression Training.ipynb
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Update Diabetes Ridge Regression Training.ipynb
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@GabbyHills
GabbyHills authored Aug 5, 2023
commit 7e07ff57e1cf16a0391b64ded07956944589bfc4
324 changes: 319 additions & 5 deletions experimentation/Diabetes Ridge Regression Training.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -13,8 +13,8 @@
"source": [
"This notebook loads the Diabetes dataset from sklearn, splits the data into training and validation sets, trains a Ridge regression model, validates the model on the validation set, and saves the model."
]
},
{
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
Expand All @@ -28,6 +28,13 @@
"import pandas as pd"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Load Data"
]
},
{
"cell_type": "code",
"execution_count": 6,
Expand All @@ -42,6 +49,228 @@
"df['Y'] = sample_data.target"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(442, 10)\n"
]
}
],
"source": [
"print(df.shape)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>age</th>\n",
" <th>sex</th>\n",
" <th>bmi</th>\n",
" <th>bp</th>\n",
" <th>s1</th>\n",
" <th>s2</th>\n",
" <th>s3</th>\n",
" <th>s4</th>\n",
" <th>s5</th>\n",
" <th>s6</th>\n",
" <th>Y</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>count</td>\n",
" <td>4.420000e+02</td>\n",
" <td>4.420000e+02</td>\n",
" <td>4.420000e+02</td>\n",
" <td>4.420000e+02</td>\n",
" <td>4.420000e+02</td>\n",
" <td>4.420000e+02</td>\n",
" <td>4.420000e+02</td>\n",
" <td>4.420000e+02</td>\n",
" <td>4.420000e+02</td>\n",
" <td>4.420000e+02</td>\n",
" <td>442.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>mean</td>\n",
" <td>-3.634285e-16</td>\n",
" <td>1.308343e-16</td>\n",
" <td>-8.045349e-16</td>\n",
" <td>1.281655e-16</td>\n",
" <td>-8.835316e-17</td>\n",
" <td>1.327024e-16</td>\n",
" <td>-4.574646e-16</td>\n",
" <td>3.777301e-16</td>\n",
" <td>-3.830854e-16</td>\n",
" <td>-3.412882e-16</td>\n",
" <td>152.133484</td>\n",
" </tr>\n",
" <tr>\n",
" <td>std</td>\n",
" <td>4.761905e-02</td>\n",
" <td>4.761905e-02</td>\n",
" <td>4.761905e-02</td>\n",
" <td>4.761905e-02</td>\n",
" <td>4.761905e-02</td>\n",
" <td>4.761905e-02</td>\n",
" <td>4.761905e-02</td>\n",
" <td>4.761905e-02</td>\n",
" <td>4.761905e-02</td>\n",
" <td>4.761905e-02</td>\n",
" <td>77.093005</td>\n",
" </tr>\n",
" <tr>\n",
" <td>min</td>\n",
" <td>-1.072256e-01</td>\n",
" <td>-4.464164e-02</td>\n",
" <td>-9.027530e-02</td>\n",
" <td>-1.123996e-01</td>\n",
" <td>-1.267807e-01</td>\n",
" <td>-1.156131e-01</td>\n",
" <td>-1.023071e-01</td>\n",
" <td>-7.639450e-02</td>\n",
" <td>-1.260974e-01</td>\n",
" <td>-1.377672e-01</td>\n",
" <td>25.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>25%</td>\n",
" <td>-3.729927e-02</td>\n",
" <td>-4.464164e-02</td>\n",
" <td>-3.422907e-02</td>\n",
" <td>-3.665645e-02</td>\n",
" <td>-3.424784e-02</td>\n",
" <td>-3.035840e-02</td>\n",
" <td>-3.511716e-02</td>\n",
" <td>-3.949338e-02</td>\n",
" <td>-3.324879e-02</td>\n",
" <td>-3.317903e-02</td>\n",
" <td>87.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>50%</td>\n",
" <td>5.383060e-03</td>\n",
" <td>-4.464164e-02</td>\n",
" <td>-7.283766e-03</td>\n",
" <td>-5.670611e-03</td>\n",
" <td>-4.320866e-03</td>\n",
" <td>-3.819065e-03</td>\n",
" <td>-6.584468e-03</td>\n",
" <td>-2.592262e-03</td>\n",
" <td>-1.947634e-03</td>\n",
" <td>-1.077698e-03</td>\n",
" <td>140.500000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>75%</td>\n",
" <td>3.807591e-02</td>\n",
" <td>5.068012e-02</td>\n",
" <td>3.124802e-02</td>\n",
" <td>3.564384e-02</td>\n",
" <td>2.835801e-02</td>\n",
" <td>2.984439e-02</td>\n",
" <td>2.931150e-02</td>\n",
" <td>3.430886e-02</td>\n",
" <td>3.243323e-02</td>\n",
" <td>2.791705e-02</td>\n",
" <td>211.500000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>max</td>\n",
" <td>1.107267e-01</td>\n",
" <td>5.068012e-02</td>\n",
" <td>1.705552e-01</td>\n",
" <td>1.320442e-01</td>\n",
" <td>1.539137e-01</td>\n",
" <td>1.987880e-01</td>\n",
" <td>1.811791e-01</td>\n",
" <td>1.852344e-01</td>\n",
" <td>1.335990e-01</td>\n",
" <td>1.356118e-01</td>\n",
" <td>346.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" age sex bmi bp s1 \\\n",
"count 4.420000e+02 4.420000e+02 4.420000e+02 4.420000e+02 4.420000e+02 \n",
"mean -3.634285e-16 1.308343e-16 -8.045349e-16 1.281655e-16 -8.835316e-17 \n",
"std 4.761905e-02 4.761905e-02 4.761905e-02 4.761905e-02 4.761905e-02 \n",
"min -1.072256e-01 -4.464164e-02 -9.027530e-02 -1.123996e-01 -1.267807e-01 \n",
"25% -3.729927e-02 -4.464164e-02 -3.422907e-02 -3.665645e-02 -3.424784e-02 \n",
"50% 5.383060e-03 -4.464164e-02 -7.283766e-03 -5.670611e-03 -4.320866e-03 \n",
"75% 3.807591e-02 5.068012e-02 3.124802e-02 3.564384e-02 2.835801e-02 \n",
"max 1.107267e-01 5.068012e-02 1.705552e-01 1.320442e-01 1.539137e-01 \n",
"\n",
" s2 s3 s4 s5 s6 \\\n",
"count 4.420000e+02 4.420000e+02 4.420000e+02 4.420000e+02 4.420000e+02 \n",
"mean 1.327024e-16 -4.574646e-16 3.777301e-16 -3.830854e-16 -3.412882e-16 \n",
"std 4.761905e-02 4.761905e-02 4.761905e-02 4.761905e-02 4.761905e-02 \n",
"min -1.156131e-01 -1.023071e-01 -7.639450e-02 -1.260974e-01 -1.377672e-01 \n",
"25% -3.035840e-02 -3.511716e-02 -3.949338e-02 -3.324879e-02 -3.317903e-02 \n",
"50% -3.819065e-03 -6.584468e-03 -2.592262e-03 -1.947634e-03 -1.077698e-03 \n",
"75% 2.984439e-02 2.931150e-02 3.430886e-02 3.243323e-02 2.791705e-02 \n",
"max 1.987880e-01 1.811791e-01 1.852344e-01 1.335990e-01 1.356118e-01 \n",
"\n",
" Y \n",
"count 442.000000 \n",
"mean 152.133484 \n",
"std 77.093005 \n",
"min 25.000000 \n",
"25% 87.000000 \n",
"50% 140.500000 \n",
"75% 211.500000 \n",
"max 346.000000 "
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# All data in a single dataframe\n",
"df.describe()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Split Data into Training and Validation Sets"
]
},
{
"cell_type": "code",
"execution_count": 12,
Expand All @@ -57,11 +286,30 @@
" \"test\": {\"X\": X_test, \"y\": y_test}}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Train Model on Training Set"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"outputs": [
{
"data": {
"text/plain": [
"Ridge(alpha=0.5, copy_X=True, fit_intercept=True, max_iter=None,\n",
" normalize=False, random_state=None, solver='auto', tol=0.001)"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# experiment parameters\n",
"args = {\n",
Expand All @@ -72,16 +320,82 @@
"reg_model.fit(data[\"train\"][\"X\"], data[\"train\"][\"y\"])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Validate Model on Validation Set"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'mse': 3298.9096058070622}\n"
]
}
],
"source": [
"preds = reg_model.predict(data[\"test\"][\"X\"])\n",
"mse = mean_squared_error(preds, y_test)\n",
"metrics = {\"mse\": mse}\n",
"print(metrics)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Save Model"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['sklearn_regression_model.pkl']"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model_name = \"sklearn_regression_model.pkl\"\n",
"\n",
"joblib.dump(value=reg, filename=model_name)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.4"
}

},
"nbformat": 4,
"nbformat_minor": 2
}