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sspai-100-hours-series-python/projects/machine-learning/mock_ml_model_for_choosing_watermelon.ipynb

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{
"cells": [
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"from sklearn.linear_model import LogisticRegression\n",
"from sklearn.preprocessing import LabelEncoder"
]
},
{
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"execution_count": 21,
"metadata": {},
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"data = [\n",
" dict(\n",
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" spot_range=\"big\",\n",
" echo=\"depressing\",\n",
" is_good=0,\n",
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" pattern=\"blured\",\n",
" spot_range=\"small\",\n",
" echo=\"depressing\",\n",
" is_good=0,\n",
" ),\n",
"]\n",
"df = pd.DataFrame(data)\n",
"df.head()\n"
]
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"metadata": {},
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{
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"{'C': 1.0,\n",
" 'class_weight': None,\n",
" 'dual': False,\n",
" 'fit_intercept': True,\n",
" 'intercept_scaling': 1,\n",
" 'l1_ratio': None,\n",
" 'max_iter': 100,\n",
" 'multi_class': 'auto',\n",
" 'n_jobs': None,\n",
" 'penalty': 'l2',\n",
" 'random_state': 0,\n",
" 'solver': 'lbfgs',\n",
" 'tol': 0.0001,\n",
" 'verbose': 0,\n",
" 'warm_start': False}"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
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"source": [
"encoder = LabelEncoder()\n",
"model = LogisticRegression(random_state=0)\n",
"\n",
"X = df.drop([\"is_good\"], axis=1).apply(encoder.fit_transform)\n",
"y = df[\"is_good\"].values\n",
"\n",
"estimator = model.fit(X, y)\n",
"estimator.get_params()"
]
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"unknown_data = [\n",
" dict(\n",
" appearance=\"broken\",\n",
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" echo=\"clear\",\n",
" ),\n",
" dict(\n",
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" pattern=\"clear\",\n",
" spot_range=\"big\",\n",
" echo=\"depressing\",\n",
" ),\n",
"]\n",
"records = pd.DataFrame(unknown_data)\n",
"target = records.apply(encoder.fit_transform)\n",
"target.head()\n"
]
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{
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"/Users/Bobot/pyenvs/pandas-startup/lib/python3.9/site-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n",
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"/Users/Bobot/pyenvs/pandas-startup/lib/python3.9/site-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n",
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