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accuracy_score\n", + "from sklearn.model_selection import GridSearchCV\n", + "from pickle import dump" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " poutcome_n previous emp.var.rate euribor3m nr.employed y_n\n", + "0 0.0 0.0 1.000000 0.980730 1.000000 0.0\n", + "1 0.0 0.0 0.333333 0.138291 0.512287 0.0\n", + "2 0.0 0.0 0.937500 0.956926 0.859735 0.0\n", + "3 0.0 0.0 0.937500 0.957379 0.859735 0.0\n", + "4 0.0 0.0 0.333333 0.175924 0.512287 0.0" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "train_data = pd.read_csv(\"../data/processed/clean_train.csv\")\n", + "test_data = pd.read_csv(\"../data/processed/clean_test.csv\")\n", + "\n", + "train_data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "X_train = train_data.drop([\"y_n\"], axis = 1)\n", + "y_train = train_data[\"y_n\"]\n", + "X_test = test_data.drop([\"y_n\"], axis = 1)\n", + "y_test = test_data[\"y_n\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
LogisticRegression()
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" + ], + "text/plain": [ + "LogisticRegression()" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model = LogisticRegression()\n", + "model.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0., 0., 0., ..., 0., 0., 0.])" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "y_pred = model.predict(X_test)\n", + "y_pred" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.8921806702282662" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "accuracy_score(y_test, y_pred)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
GridSearchCV(cv=10, estimator=LogisticRegression(),\n",
+                            "             param_grid={'C': [0.001, 0.01, 0.1, 1, 10, 100, 1000],\n",
+                            "                         'penalty': ['l1', 'l2', 'elasticnet', None],\n",
+                            "                         'solver': ['newton-cg', 'lbfgs', 'liblinear', 'sag',\n",
+                            "                                    'saga']},\n",
+                            "             scoring='accuracy')
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "GridSearchCV(cv=10, estimator=LogisticRegression(),\n", + " param_grid={'C': [0.001, 0.01, 0.1, 1, 10, 100, 1000],\n", + " 'penalty': ['l1', 'l2', 'elasticnet', None],\n", + " 'solver': ['newton-cg', 'lbfgs', 'liblinear', 'sag',\n", + " 'saga']},\n", + " scoring='accuracy')" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "hyperparams = {\n", + " \"C\": [0.001, 0.01, 0.1, 1, 10, 100, 1000],\n", + " \"penalty\": [\"l1\", \"l2\", \"elasticnet\", None],\n", + " \"solver\": [\"newton-cg\", \"lbfgs\", \"liblinear\", \"sag\", \"saga\"]\n", + "}\n", + "\n", + "grid = GridSearchCV(model, hyperparams, scoring = \"accuracy\", cv = 10)\n", + "grid" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Best hyperparameters: {'C': 0.1, 'penalty': 'l2', 'solver': 'liblinear'}\n" + ] + } + ], + "source": [ + "def warn(*args, **kwargs):\n", + " pass\n", + "import warnings\n", + "warnings.warn = warn\n", + "\n", + "grid.fit(X_train, y_train)\n", + "\n", + "print(f\"Best hyperparameters: {grid.best_params_}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
LogisticRegression(C=0.1, solver='liblinear')
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "LogisticRegression(C=0.1, solver='liblinear')" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model = LogisticRegression(C = 0.1, penalty = \"l2\", solver = \"liblinear\")\n", + "model.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0., 0., 0., ..., 0., 0., 0.])" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "y_pred = model.predict(X_test)\n", + "y_pred" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.8938805245264692" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "accuracy_score(y_test, y_pred)" + ] + }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "# Your code here" + "dump(model, open(\"../models/logistic_regression_C-0.1_penalty-l2_solver-liblinear_42.sav\", \"wb\"))" ] } ], @@ -33,7 +1274,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.13" + "version": "3.11.4" }, "orig_nbformat": 4, "vscode": {