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ML-For-Beginners
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Merge branch 'main' of https://github.com/PaskalSunari/ML-For-Beginners into ML-Patch-2

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Paskal 1 week ago
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commit c3e3d2299f
2 changed files with 656 additions and 13 deletions
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4-Classification/1-Introduction/notebook.ipynb
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4-Classification/2-Classifiers-1/notebook.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Build Classification Models"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## predict a national cuisine"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"data": {
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" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Unnamed: 0</th>\n",
" <th>cuisine</th>\n",
" <th>almond</th>\n",
" <th>angelica</th>\n",
" <th>anise</th>\n",
" <th>anise_seed</th>\n",
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" <th>...</th>\n",
" <th>whiskey</th>\n",
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" <th>whole_grain_wheat_flour</th>\n",
" <th>wine</th>\n",
" <th>wood</th>\n",
" <th>yam</th>\n",
" <th>yeast</th>\n",
" <th>yogurt</th>\n",
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" </tr>\n",
" </thead>\n",
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" <td>indian</td>\n",
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" <td>0</td>\n",
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" <td>1</td>\n",
" <td>0</td>\n",
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" </tbody>\n",
"</table>\n",
"<p>5 rows × 382 columns</p>\n",
"</div>"
],
"text/plain": [
" Unnamed: 0 cuisine almond angelica anise anise_seed apple \\\n",
"0 0 indian 0 0 0 0 0 \n",
"1 1 indian 1 0 0 0 0 \n",
"2 2 indian 0 0 0 0 0 \n",
"3 3 indian 0 0 0 0 0 \n",
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"\n",
" apple_brandy apricot armagnac ... whiskey white_bread white_wine \\\n",
"0 0 0 0 ... 0 0 0 \n",
"1 0 0 0 ... 0 0 0 \n",
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"3 0 0 0 ... 0 0 0 \n",
"4 0 0 0 ... 0 0 0 \n",
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" whole_grain_wheat_flour wine wood yam yeast yogurt zucchini \n",
"0 0 0 0 0 0 0 0 \n",
"1 0 0 0 0 0 0 0 \n",
"2 0 0 0 0 0 0 0 \n",
"3 0 0 0 0 0 0 0 \n",
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"\n",
"[5 rows x 382 columns]"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas as pd\n",
"cuisines_df = pd.read_csv(\"../data/cleaned_cuisines.csv\")\n",
"cuisines_df.head()"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.linear_model import LogisticRegression\n",
"from sklearn.model_selection import train_test_split, cross_val_score\n",
"from sklearn.metrics import accuracy_score, confusion_matrix, classification_report, precision_recall_curve, precision_score\n",
"from sklearn.svm import SVC\n",
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 indian\n",
"1 indian\n",
"2 indian\n",
"3 indian\n",
"4 indian\n",
"Name: cuisine, dtype: object"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cuisines_label_df = cuisines_df['cuisine']\n",
"cuisines_label_df.head()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>almond</th>\n",
" <th>angelica</th>\n",
" <th>anise</th>\n",
" <th>anise_seed</th>\n",
" <th>apple</th>\n",
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" almond angelica anise anise_seed apple apple_brandy apricot \\\n",
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]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cusisines_features_df = cuisines_df.drop(['Unnamed: 0', 'cuisine'], axis=1)\n",
"cusisines_features_df.head()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"X_train, X_test, y_train, y_test = train_test_split(cusisines_features_df, cuisines_label_df, test_size=0.3, random_state=42)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Logistic Regression Accuracy: 0.79\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/workspaces/ML-For-Beginners/.venv/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1256: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. Use OneVsRestClassifier(LogisticRegression(..)) instead. Leave it to its default value to avoid this warning.\n",
" warnings.warn(\n"
]
}
],
"source": [
"lr = LogisticRegression(multi_class = 'ovr', solver='liblinear')\n",
"model = lr.fit(X_train, np.ravel(y_train))\n",
"accuracy = model.score(X_test, y_test)\n",
"print(f\"Logistic Regression Accuracy: {accuracy:.2f}\")\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"ingredients: Index(['fish', 'lime_juice', 'shallot'], dtype='object')\n",
"cuisine: thai\n"
]
}
],
"source": [
"print(f'ingredients: {X_test.iloc[50][X_test.iloc[50]!=0].keys()}')\n",
"print(f'cuisine: {y_test.iloc[50]}')"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/workspaces/ML-For-Beginners/.venv/lib/python3.11/site-packages/sklearn/utils/validation.py:2739: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n",
" warnings.warn(\n"
]
},
{
"data": {
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" <th>japanese</th>\n",
" <td>0.134624</td>\n",
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" <tr>\n",
" <th>chinese</th>\n",
" <td>0.014531</td>\n",
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" <tr>\n",
" <th>korean</th>\n",
" <td>0.008383</td>\n",
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"text/plain": [
" 0\n",
"thai 0.839568\n",
"japanese 0.134624\n",
"chinese 0.014531\n",
"korean 0.008383\n",
"indian 0.002894"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test= X_test.iloc[50].values.reshape(-1, 1).T\n",
"proba = model.predict_proba(test)\n",
"classes = model.classes_\n",
"resultdf = pd.DataFrame(data=proba, columns=classes)\n",
"\n",
"topPrediction = resultdf.T.sort_values(by=[0], ascending = [False])\n",
"topPrediction.head()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" precision recall f1-score support\n",
"\n",
" chinese 0.69 0.69 0.69 236\n",
" indian 0.91 0.91 0.91 245\n",
" japanese 0.73 0.72 0.73 231\n",
" korean 0.81 0.76 0.78 242\n",
" thai 0.78 0.84 0.81 245\n",
"\n",
" accuracy 0.79 1199\n",
" macro avg 0.79 0.78 0.78 1199\n",
"weighted avg 0.79 0.79 0.79 1199\n",
"\n"
]
}
],
"source": [
"y_pred = model.predict(X_test)\n",
"print(classification_report(y_test, y_pred))"
]
}
],
"metadata": {
"kernelspec": {
"display_name": ".venv",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
@ -10,19 +662,10 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": 3
"version": "3.11.4"
},
"orig_nbformat": 2
},
"nbformat": 4,
"nbformat_minor": 2,
"cells": [
{
"source": [
"# Build Classification Models"
],
"cell_type": "markdown",
"metadata": {}
}
]
"nbformat_minor": 2
}
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