KNN.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8c402ceb-8a65-4437-b75e-037fd6dfa9e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.metrics import confusion_matrix, classification_report\n",
    "import multiprocessing\n",
    "from sklearn.metrics import mean_squared_error\n",
    "import numpy as np\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "import xgboost as xgb\n",
    "import json\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from striprtf.striprtf import rtf_to_text\n",
    "from IPython.display import display\n",
    "import warnings\n",
    "from sklearn.exceptions import ConvergenceWarning\n",
    "\n",
    "\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import confusion_matrix, classification_report\n",
    "\n",
    "from sklearn.feature_extraction.text import HashingVectorizer\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "warnings.filterwarnings(\"ignore\", category=ConvergenceWarning)\n",
    "\n",
    "\n",
    "def hackathon_problem(json_file_path):\n",
    "    # Read JSON file\n",
    "    with open(json_file_path, \"r\") as file:\n",
    "        data = file.read()\n",
    "        text = rtf_to_text(data)  \n",
    "        json_data = json.loads(text)\n",
    "\n",
    "    # Load Data\n",
    "    data = pd.read_csv(json_data[\"design_state_data\"][\"session_info\"][\"dataset\"])\n",
    "\n",
    "    # Encode categorical columns\n",
    "    label_encoders = {}\n",
    "    for column in data.columns:\n",
    "        if data[column].dtype == 'object':\n",
    "            label_encoders[column] = LabelEncoder()\n",
    "            data[column] = label_encoders[column].fit_transform(data[column])\n",
    "\n",
    "    # Define preprocessing steps based on JSON data\n",
    "    preprocessing_steps = []\n",
    "    for feature, details in json_data[\"design_state_data\"][\"feature_handling\"].items():\n",
    "        if \"missing_values\" in details and details[\"missing_values\"] == \"Impute\":\n",
    "            if details[\"impute_with\"] == \"Average of values\":\n",
    "                strategy = 'mean'\n",
    "            else:\n",
    "                strategy = 'median'\n",
    "            preprocessing_steps.append((feature + '_imputer', SimpleImputer(strategy=strategy)))\n",
    "\n",
    "    # Apply preprocessing steps\n",
    "    for step in preprocessing_steps:\n",
    "        feature_name, transformer = step\n",
    "        data[feature_name] = transformer.fit_transform(data[[feature_name]])\n",
    "\n",
    "    # Separate X and y\n",
    "    selected_features = [feature for feature, details in json_data[\"design_state_data\"][\"feature_handling\"].items() if details[\"is_selected\"]]\n",
    "    X = data[selected_features]\n",
    "    Y = data[json_data[\"design_state_data\"][\"target\"][\"target\"]]\n",
    "\n",
    "\n",
    "    # Split Data\n",
    "    train_ratio = json_data[\"design_state_data\"][\"train\"][\"train_ratio\"]\n",
    "    random_seed = json_data[\"design_state_data\"][\"train\"][\"random_seed\"]\n",
    "    X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=1 - train_ratio, random_state=random_seed)\n",
    "\n",
    "    \n",
    "    \n",
    "    # Get the number of available CPU cores for parallelism\n",
    "    num_cores = multiprocessing.cpu_count()\n",
    "    \n",
    "    selected_algorithm = None\n",
    "    for algorithm, details in json_data[\"design_state_data\"][\"algorithms\"].items():\n",
    "        if details[\"is_selected\"]:\n",
    "            selected_algorithm = algorithm\n",
    "            break\n",
    "    \n",
    "    if selected_algorithm == \"RandomForestClassifier\":\n",
    "        model = RandomForestClassifier()\n",
    "        parameters = {\n",
    "            \"n_estimators\": np.linspace(details[\"min_trees\"], details[\"max_trees\"], num=3, dtype=int),\n",
    "            \"max_depth\": np.linspace(details[\"min_depth\"], details[\"max_depth\"], num=3, dtype=int),\n",
    "            \"min_samples_leaf\": np.linspace(details[\"min_samples_per_leaf_min_value\"], details[\"min_samples_per_leaf_max_value\"], num=3, dtype=int)\n",
    "        }\n",
    "        # Modify GridSearchCV instantiation to use parallel processing\n",
    "        grid_search = GridSearchCV(model, parameters, cv=5, n_jobs=num_cores)\n",
    "        grid_search.fit(X_train, Y_train)\n",
    "        best_model = grid_search.best_estimator_\n",
    "\n",
    "\n",
    "    # Evaluate Models\n",
    "        Y_pred = best_model.predict(X_test)\n",
    "\n",
    "\n",
    "        confusion_mat = confusion_matrix(Y_test, Y_pred)\n",
    "\n",
    "        # Create heatmap with seaborn\n",
    "        sns.heatmap(confusion_mat, annot=True, cmap=\"viridis\", fmt=\"d\", cbar=False,\n",
    "                    linewidths=0.5, linecolor='gray', square=True,\n",
    "                    xticklabels=True, yticklabels=True, annot_kws={\"size\": 10})\n",
    "        \n",
    "        # Customize axis labels\n",
    "        plt.xlabel(\"Predicted Labels\")\n",
    "        plt.ylabel(\"True Labels\")\n",
    "        # Show plot\n",
    "        plt.show()      \n",
    "        # Assign value to classification_rep_df\n",
    "        classification_rep_dict = classification_report(Y_test, Y_pred, output_dict=True)\n",
    "        classification_rep_df = pd.DataFrame(classification_rep_dict)\n",
    "        # Add some styling to the DataFrame\n",
    "        classification_rep_styled = classification_rep_df.style.background_gradient(cmap='viridis')\n",
    "        # Inside each block where you print the classification report, replace the print statement with the following:\n",
    "        # Print the styled classification report\n",
    "        print(\"Classification Report:\")\n",
    "        display(classification_rep_styled)\n",
    "\n",
    "    \n",
    "            \n",
    "    if selected_algorithm == \"RandomForestRegressor\":\n",
    "        # Your RandomForestRegressor code\n",
    "        model = RandomForestRegressor()\n",
    "        parameters = {\n",
    "            \"n_estimators\": list(range(details[\"min_trees\"], details[\"max_trees\"] + 1)),\n",
    "            \"max_depth\": list(range(details[\"min_depth\"], details[\"max_depth\"] + 1)),\n",
    "            \"min_samples_leaf\": list(range(details[\"min_samples_per_leaf_min_value\"], details[\"min_samples_per_leaf_max_value\"] + 1))}\n",
    "        # Modify GridSearchCV instantiation to use parallel processing\n",
    "        grid_search = GridSearchCV(model, parameters, cv=5, n_jobs=num_cores)\n",
    "        grid_search.fit(X_train, Y_train)\n",
    "        best_model = grid_search.best_estimator_\n",
    "    \n",
    "        # Predict on test data\n",
    "        Y_pred = best_model.predict(X_test)\n",
    "        \n",
    "        # Calculate R-squared\n",
    "        r_squared = best_model.score(X_test, Y_test)\n",
    "        \n",
    "        # Calculate adjusted R-squared\n",
    "        n = len(Y_test)\n",
    "        k = X_test.shape[1]  # Number of predictors\n",
    "        adjusted_r_squared = 1 - (1 - r_squared) * (n - 1) / (n - k - 1)\n",
    "        rmse = np.sqrt(mean_squared_error(Y_test, Y_pred))\n",
    "        \n",
    "        # Create DataFrame for metrics\n",
    "        \n",
    "        metrics_df = pd.DataFrame({\n",
    "            'Metric': ['R-squared', 'Adjusted R-squared', 'Root Mean Squared Error (RMSE)'],\n",
    "            'Value': [r_squared, adjusted_r_squared, rmse]\n",
    "        })\n",
    "    \n",
    "        # Style DataFrame\n",
    "        styled_metrics_df = (\n",
    "            metrics_df.style\n",
    "                .set_properties(**{'text-align': 'left'})  # Align text to the left\n",
    "                .highlight_max(color='lightgreen')         # Highlight maximum value\n",
    "                .set_caption('Model Evaluation Metrics')   # Add caption\n",
    "        )\n",
    "    \n",
    "        # Display styled DataFrame\n",
    "        display(styled_metrics_df)\n",
    "\n",
    "        \n",
    "    if selected_algorithm == \"LinearRegression\":\n",
    "        # Your LinearRegression code\n",
    "        best_model = LinearRegression()\n",
    "        best_model.fit(X_train, Y_train)\n",
    "    \n",
    "        # Predict on test data\n",
    "        Y_pred = best_model.predict(X_test)\n",
    "        \n",
    "        # Calculate R-squared\n",
    "        r_squared = best_model.score(X_test, Y_test)\n",
    "        \n",
    "        # Calculate adjusted R-squared\n",
    "        n = len(Y_test)\n",
    "        k = X_test.shape[1]  # Number of predictors\n",
    "        adjusted_r_squared = 1 - (1 - r_squared) * (n - 1) / (n - k - 1)\n",
    "        rmse = np.sqrt(mean_squared_error(Y_test, Y_pred))\n",
    "\n",
    "        # Create DataFrame for metrics\n",
    "        metrics_df = pd.DataFrame({\n",
    "            'Metric': ['R-squared', 'Adjusted R-squared', 'Root Mean Squared Error (RMSE)'],\n",
    "            'Value': [r_squared, adjusted_r_squared, rmse]\n",
    "        })\n",
    "    \n",
    "        # Style DataFrame\n",
    "        styled_metrics_df = (\n",
    "            metrics_df.style\n",
    "                .set_properties(**{'text-align': 'left'})  # Align text to the left\n",
    "                .highlight_max(color='lightgreen')         # Highlight maximum value\n",
    "                .set_caption('Model Evaluation Metrics')   # Add caption\n",
    "        )\n",
    "    \n",
    "        # Display styled DataFrame\n",
    "        display(styled_metrics_df)\n",
    "\n",
    "    if selected_algorithm == \"LogisticRegression\":\n",
    "        model = LogisticRegression()\n",
    "        parameters = {\n",
    "            \"C\": np.linspace(details[\"min_regparam\"], details[\"max_regparam\"], num=5),\n",
    "            \"max_iter\": np.linspace(details[\"min_iter\"], details[\"max_iter\"], num=5, dtype=int),\n",
    "            \"l1_ratio\": np.linspace(details[\"min_elasticnet\"], details[\"max_elasticnet\"], num=5)\n",
    "        }\n",
    "        \n",
    "        # Modify GridSearchCV instantiation to use parallel processing\n",
    "        grid_search = GridSearchCV(model, parameters, cv=5, n_jobs=num_cores)\n",
    "        grid_search.fit(X_train, Y_train)\n",
    "        best_model = grid_search.best_estimator_\n",
    "    \n",
    "        # Evaluate Models\n",
    "        Y_pred = best_model.predict(X_test)\n",
    "\n",
    "\n",
    "        \n",
    "        confusion_mat = confusion_matrix(Y_test, Y_pred)\n",
    "\n",
    "        # Create heatmap with seaborn\n",
    "        sns.heatmap(confusion_mat, annot=True, cmap=\"viridis\", fmt=\"d\", cbar=False,\n",
    "                    linewidths=0.5, linecolor='gray', square=True,\n",
    "                    xticklabels=True, yticklabels=True, annot_kws={\"size\": 10})\n",
    "        \n",
    "        # Customize axis labels\n",
    "        plt.xlabel(\"Predicted Labels\")\n",
    "        plt.ylabel(\"True Labels\")\n",
    "        # Show plot\n",
    "        plt.show()      \n",
    "        # Assign value to classification_rep_df\n",
    "        classification_rep_dict = classification_report(Y_test, Y_pred, output_dict=True)\n",
    "        classification_rep_df = pd.DataFrame(classification_rep_dict)\n",
    "        # Add some styling to the DataFrame\n",
    "        classification_rep_styled = classification_rep_df.style.background_gradient(cmap='viridis')\n",
    "        # Inside each block where you print the classification report, replace the print statement with the following:\n",
    "        # Print the styled classification report\n",
    "        print(\"Classification Report:\")\n",
    "        display(classification_rep_styled)\n",
    "\n",
    "    \n",
    "        \n",
    "    if selected_algorithm in [\"RidgeRegression\", \"LassoRegression\"]:\n",
    "        if selected_algorithm == \"RidgeRegression\":\n",
    "            model = Ridge()\n",
    "        elif selected_algorithm == \"LassoRegression\":\n",
    "            model = Lasso()\n",
    "    \n",
    "        parameters = {\n",
    "            \"alpha\": [i/10 for i in range(int(details[\"min_regparam\"]*10), int(details[\"max_regparam\"]*10)+1)],\n",
    "            \"max_iter\": list(range(details[\"min_iter\"], details[\"max_iter\"] + 1))}\n",
    "        # Modify GridSearchCV instantiation to use parallel processing\n",
    "        grid_search = GridSearchCV(model, parameters, cv=5)\n",
    "        grid_search.fit(X_train, Y_train)\n",
    "        best_model = grid_search.best_estimator_\n",
    "    \n",
    "        # Evaluate Models\n",
    "        Y_pred = best_model.predict(X_test)\n",
    "        \n",
    "        # Calculate R-squared\n",
    "        r_squared = best_model.score(X_test, Y_test)\n",
    "        \n",
    "        # Calculate adjusted R-squared\n",
    "        n = len(Y_test)\n",
    "        k = X_test.shape[1]  # Number of predictors\n",
    "        adjusted_r_squared = 1 - (1 - r_squared) * (n - 1) / (n - k - 1)\n",
    "        rmse = np.sqrt(mean_squared_error(Y_test, Y_pred))\n",
    "\n",
    "        # Create DataFrame for metrics\n",
    "        metrics_df = pd.DataFrame({\n",
    "            'Metric': ['R-squared', 'Adjusted R-squared', 'Root Mean Squared Error (RMSE)'],\n",
    "            'Value': [r_squared, adjusted_r_squared, rmse]\n",
    "        })\n",
    "    \n",
    "        # Style DataFrame\n",
    "        styled_metrics_df = (\n",
    "            metrics_df.style\n",
    "                .set_properties(**{'text-align': 'left'})  # Align text to the left\n",
    "                .highlight_max(color='lightgreen')         # Highlight maximum value\n",
    "                .set_caption('Model Evaluation Metrics')   # Add caption\n",
    "        )\n",
    "    \n",
    "        # Display styled DataFrame\n",
    "        display(styled_metrics_df)\n",
    "\n",
    "        \n",
    "    if selected_algorithm == \"ElasticNetRegression\":\n",
    "        model = ElasticNet()\n",
    "        # Hyperparameters\n",
    "        parameters = {\n",
    "            \"alpha\": [i/10 for i in range(int(details[\"min_regparam\"]*10), int(details[\"max_regparam\"]*10)+1)],\n",
    "            \"l1_ratio\": [i/10 for i in range(int(details[\"min_elasticnet\"]*10), int(details[\"max_elasticnet\"]*10)+1)],\n",
    "            \"max_iter\": list(range(details[\"min_iter\"], details[\"max_iter\"] + 1))}\n",
    "        # Modify GridSearchCV instantiation to use parallel processing\n",
    "        grid_search = GridSearchCV(model, parameters, cv=5)\n",
    "        grid_search.fit(X_train, Y_train)\n",
    "        best_model = grid_search.best_estimator_\n",
    "    \n",
    "        # Evaluate Models\n",
    "        Y_pred = best_model.predict(X_test)\n",
    "        \n",
    "        # Calculate R-squared\n",
    "        r_squared = best_model.score(X_test, Y_test)\n",
    "        \n",
    "        # Calculate adjusted R-squared\n",
    "        n = len(Y_test)\n",
    "        k = X_test.shape[1]  # Number of predictors\n",
    "        adjusted_r_squared = 1 - (1 - r_squared) * (n - 1) / (n - k - 1)\n",
    "        \n",
    "        # Calculate RMSE\n",
    "        rmse = np.sqrt(mean_squared_error(Y_test, Y_pred))\n",
    "\n",
    "        # Create DataFrame for metrics\n",
    "        metrics_df = pd.DataFrame({\n",
    "            'Metric': ['R-squared', 'Adjusted R-squared', 'Root Mean Squared Error (RMSE)'],\n",
    "            'Value': [r_squared, adjusted_r_squared, rmse]\n",
    "        })\n",
    "    \n",
    "        # Style DataFrame\n",
    "        styled_metrics_df = (\n",
    "            metrics_df.style\n",
    "                .set_properties(**{'text-align': 'left'})  # Align text to the left\n",
    "                .highlight_max(color='lightgreen')         # Highlight maximum value\n",
    "                .set_caption('Model Evaluation Metrics')   # Add caption\n",
    "        )\n",
    "    \n",
    "        # Display styled DataFrame\n",
    "        display(styled_metrics_df)\n",
    "\n",
    "        \n",
    "    if selected_algorithm == \"xg_boost\":\n",
    "        # XGBoost specific handling\n",
    "        model = xgb.XGBClassifier(objective='multi:softmax', \n",
    "                                   booster='dart' if details['dart'] else 'gbtree',\n",
    "                                   tree_method = details['tree_method'] if details['tree_method'] != \"\" else \"auto\",\n",
    "                                   random_state=details['random_state'],)\n",
    "        parameters = {\n",
    "            'n_estimators': [details[\"max_num_of_trees\"]] if details[\"max_num_of_trees\"] > 0 else [5],\n",
    "            'max_depth': details['max_depth_of_tree'],\n",
    "            'learning_rate': [value * 0.001 for value in details['learningRate']],\n",
    "            'reg_alpha':  [value * 0.01 for value in details['l1_regularization']],\n",
    "            'reg_lambda':  [value * 0.01 for value in details['l2_regularization']],\n",
    "            'gamma': [value * 0.01 for value in details['gamma']],\n",
    "            'min_child_weight': [value * 0.01 for value in details['min_child_weight']],\n",
    "            'subsample': [value * 0.01 for value in details['sub_sample']],\n",
    "            'colsample_bytree': [value * 0.01 for value in details['col_sample_by_tree']]\n",
    "        }\n",
    "       \n",
    "        # Perform grid search with cross-validation\n",
    "        grid_search = GridSearchCV(model, parameters, cv=5 , n_jobs=num_cores)\n",
    "        \n",
    "        # Fit the model with early stopping on the validation set\n",
    "        grid_search.fit(X_train, Y_train, eval_set=[(X_test, Y_test)], early_stopping_rounds=details['early_stopping_rounds'] if details['early_stopping'] else None)\n",
    "        \n",
    "        # Get the best model from grid search\n",
    "        best_model = grid_search.best_estimator_\n",
    "        \n",
    "        # Make predictions on the test set\n",
    "        Y_pred = best_model.predict(X_test)\n",
    "        \n",
    "\n",
    "\n",
    "        confusion_mat = confusion_matrix(Y_test, Y_pred)\n",
    "\n",
    "        # Create heatmap with seaborn\n",
    "        sns.heatmap(confusion_mat, annot=True, cmap=\"viridis\", fmt=\"d\", cbar=False,\n",
    "                    linewidths=0.5, linecolor='gray', square=True,\n",
    "                    xticklabels=True, yticklabels=True, annot_kws={\"size\": 10})\n",
    "        \n",
    "        # Customize axis labels\n",
    "        plt.xlabel(\"Predicted Labels\")\n",
    "        plt.ylabel(\"True Labels\")\n",
    "        # Show plot\n",
    "        plt.show()      \n",
    "        # Assign value to classification_rep_df\n",
    "        classification_rep_dict = classification_report(Y_test, Y_pred, output_dict=True)\n",
    "        classification_rep_df = pd.DataFrame(classification_rep_dict)\n",
    "        # Add some styling to the DataFrame\n",
    "        classification_rep_styled = classification_rep_df.style.background_gradient(cmap='viridis')\n",
    "        # Inside each block where you print the classification report, replace the print statement with the following:\n",
    "        # Print the styled classification report\n",
    "        print(\"Classification Report:\")\n",
    "        display(classification_rep_styled)\n",
    "\n",
    "    \n",
    "    if selected_algorithm == \"DecisionTreeClassifier\":\n",
    "        # Decision Tree Classifier specific handling\n",
    "        criterion = 'gini' if details['use_gini'] else 'entropy'\n",
    "        # Fix the following line to use 'use_entropy' instead of 'use_best'\n",
    "        splitter = 'best' if details['use_best'] and not details['use_random'] else 'random'\n",
    "        \n",
    "        model = DecisionTreeClassifier(criterion=criterion, splitter=splitter)\n",
    "        parameters = {\n",
    "            'max_depth': list(range(details['min_depth'], details['max_depth'] + 1)),\n",
    "            'min_samples_leaf': details['min_samples_per_leaf']}\n",
    "        grid_search = GridSearchCV(model, parameters, cv=5)\n",
    "        grid_search.fit(X_train, Y_train)\n",
    "        best_model = grid_search.best_estimator_\n",
    "        Y_pred = best_model.predict(X_test)\n",
    "\n",
    "\n",
    "        confusion_mat = confusion_matrix(Y_test, Y_pred)\n",
    "\n",
    "        # Create heatmap with seaborn\n",
    "        sns.heatmap(confusion_mat, annot=True, cmap=\"viridis\", fmt=\"d\", cbar=False,\n",
    "                    linewidths=0.5, linecolor='gray', square=True,\n",
    "                    xticklabels=True, yticklabels=True, annot_kws={\"size\": 10})\n",
    "        \n",
    "        # Customize axis labels\n",
    "        plt.xlabel(\"Predicted Labels\")\n",
    "        plt.ylabel(\"True Labels\")\n",
    "        # Show plot\n",
    "        plt.show()      \n",
    "        # Assign value to classification_rep_df\n",
    "        classification_rep_dict = classification_report(Y_test, Y_pred, output_dict=True)\n",
    "        classification_rep_df = pd.DataFrame(classification_rep_dict)\n",
    "        # Add some styling to the DataFrame\n",
    "        classification_rep_styled = classification_rep_df.style.background_gradient(cmap='viridis')\n",
    "        # Inside each block where you print the classification report, replace the print statement with the following:\n",
    "        # Print the styled classification report\n",
    "        print(\"Classification Report:\")\n",
    "        display(classification_rep_styled)\n",
    "\n",
    "      \n",
    "    if selected_algorithm == \"DecisionTreeRegressor\":\n",
    "        # Decision Tree Regressor specific handling\n",
    "        splitter = 'best' if details.get('use_best', False) and not details.get('use_random', False) else 'random'\n",
    "        random_state = details.get('random_state', 10)  # Use the provided random state or default to 10\n",
    "        \n",
    "        model = DecisionTreeRegressor( splitter=splitter, random_state=random_state)\n",
    "        parameters = {\n",
    "            'max_depth': list(range(details['min_depth'], details['max_depth'] + 1)),\n",
    "            'min_samples_leaf': details['min_samples_per_leaf']\n",
    "        }\n",
    "        \n",
    "        grid_search = GridSearchCV(model, parameters, cv=5)\n",
    "        grid_search.fit(X_train, Y_train)\n",
    "        best_model = grid_search.best_estimator_\n",
    "        \n",
    "        # Predict on test data\n",
    "        Y_pred = best_model.predict(X_test)\n",
    "        \n",
    "        # Calculate R-squared\n",
    "        r_squared = best_model.score(X_test, Y_test)\n",
    "        \n",
    "        # Calculate adjusted R-squared\n",
    "        n = len(Y_test)\n",
    "        k = X_test.shape[1]  # Number of predictors\n",
    "        adjusted_r_squared = 1 - (1 - r_squared) * (n - 1) / (n - k - 1)\n",
    "\n",
    "        rmse = np.sqrt(mean_squared_error(Y_test, Y_pred))\n",
    "\n",
    "        # Create DataFrame for metrics\n",
    "        metrics_df = pd.DataFrame({\n",
    "            'Metric': ['R-squared', 'Adjusted R-squared', 'Root Mean Squared Error (RMSE)'],\n",
    "            'Value': [r_squared, adjusted_r_squared, rmse]\n",
    "        })\n",
    "    \n",
    "        # Style DataFrame\n",
    "        styled_metrics_df = (\n",
    "            metrics_df.style\n",
    "                .set_properties(**{'text-align': 'left'})  # Align text to the left\n",
    "                .highlight_max(color='lightgreen')         # Highlight maximum value\n",
    "                .set_caption('Model Evaluation Metrics')   # Add caption\n",
    "        )\n",
    "    \n",
    "        # Display styled DataFrame\n",
    "        display(styled_metrics_df)\n",
    "\n",
    "        \n",
    "    if selected_algorithm == \"SVM\":\n",
    "        # SVM specific handling\n",
    "        kernels = []\n",
    "        if details['linear_kernel']:\n",
    "            kernels.append('linear')\n",
    "        if details['rep_kernel']:\n",
    "            kernels.append('rbf')\n",
    "        if details['polynomial_kernel']:\n",
    "            kernels.append('poly')\n",
    "        if details['sigmoid_kernel']:\n",
    "            kernels.append('sigmoid')\n",
    "        \n",
    "        model = SVC()\n",
    "        parameters = {\n",
    "            'C': details['c_value'],\n",
    "            'kernel': kernels,\n",
    "            'gamma': ['auto', 'scale'] if details['scale'] else details['custom_gamma_values'],\n",
    "            'tol': [10 ** -details['tolerance']],\n",
    "            'max_iter': [details['max_iterations']]\n",
    "        }\n",
    "        grid_search = GridSearchCV(model, parameters, cv=5)\n",
    "        grid_search.fit(X_train, Y_train)\n",
    "        best_model = grid_search.best_estimator_\n",
    "        \n",
    "        # Predict on test data\n",
    "        Y_pred = best_model.predict(X_test)\n",
    "        \n",
    "\n",
    "\n",
    "        confusion_mat = confusion_matrix(Y_test, Y_pred)\n",
    "\n",
    "        # Create heatmap with seaborn\n",
    "        sns.heatmap(confusion_mat, annot=True, cmap=\"viridis\", fmt=\"d\", cbar=False,\n",
    "                    linewidths=0.5, linecolor='gray', square=True,\n",
    "                    xticklabels=True, yticklabels=True, annot_kws={\"size\": 10})\n",
    "        \n",
    "        # Customize axis labels\n",
    "        plt.xlabel(\"Predicted Labels\")\n",
    "        plt.ylabel(\"True Labels\")\n",
    "        # Show plot\n",
    "        plt.show()      \n",
    "        # Assign value to classification_rep_df\n",
    "        classification_rep_dict = classification_report(Y_test, Y_pred, output_dict=True)\n",
    "        classification_rep_df = pd.DataFrame(classification_rep_dict)\n",
    "        # Add some styling to the DataFrame\n",
    "        classification_rep_styled = classification_rep_df.style.background_gradient(cmap='viridis')\n",
    "        # Inside each block where you print the classification report, replace the print statement with the following:\n",
    "        # Print the styled classification report\n",
    "        print(\"Classification Report:\")\n",
    "        display(classification_rep_styled)\n",
    "\n",
    "    \n",
    "    \n",
    "    if selected_algorithm == \"KNN\":\n",
    "        model = KNeighborsClassifier()\n",
    "        parameters = {\n",
    "            'n_neighbors': details['k_value'],\n",
    "            'weights': ['uniform', 'distance'] if details['distance_weighting'] else ['uniform'],\n",
    "            'algorithm': ['auto'] if details['neighbour_finding_algorithm'] == \"Automatic\" else [details['neighbour_finding_algorithm']],\n",
    "            'p': [details['p_value']] if details['p_value'] > 0 else [1]\n",
    "        }\n",
    "        grid_search = GridSearchCV(model, parameters, cv=5)\n",
    "        grid_search.fit(X_train, Y_train)\n",
    "        best_model = grid_search.best_estimator_\n",
    "        Y_pred = best_model.predict(X_test)\n",
    "\n",
    "        confusion_mat = confusion_matrix(Y_test, Y_pred)\n",
    "\n",
    "        # Create heatmap with seaborn\n",
    "        sns.heatmap(confusion_mat, annot=True, cmap=\"viridis\", fmt=\"d\", cbar=False,\n",
    "                    linewidths=0.5, linecolor='gray', square=True,\n",
    "                    xticklabels=True, yticklabels=True, annot_kws={\"size\": 10})\n",
    "        \n",
    "        # Customize axis labels\n",
    "        plt.xlabel(\"Predicted Labels\")\n",
    "        plt.ylabel(\"True Labels\")\n",
    "        # Show plot\n",
    "        plt.show()      \n",
    "        # Assign value to classification_rep_df\n",
    "        classification_rep_dict = classification_report(Y_test, Y_pred, output_dict=True)\n",
    "        classification_rep_df = pd.DataFrame(classification_rep_dict)\n",
    "        # Add some styling to the DataFrame\n",
    "        classification_rep_styled = classification_rep_df.style.background_gradient(cmap='viridis')\n",
    "        # Inside each block where you print the classification report, replace the print statement with the following:\n",
    "        # Print the styled classification report\n",
    "        print(\"Classification Report:\")\n",
    "        display(classification_rep_styled)\n",
    "\n",
    "    \n",
    "    if selected_algorithm == \"neural_network\":\n",
    "        # Neural Network specific handling\n",
    "  # Initialize the MLPClassifier model with early stopping parameter\n",
    "        model = MLPClassifier(early_stopping=details['early_stopping'])\n",
    "\n",
    "        # Define the parameters for grid search\n",
    "        parameters = {\n",
    "            'hidden_layer_sizes': details['hidden_layer_sizes'],\n",
    "            'activation': ['identity', 'logistic', 'tanh', 'relu'],\n",
    "            'alpha': [details['alpha_value']] if details['alpha_value'] > 0 else [.1],\n",
    "            'max_iter': [details['max_iterations']] if details['max_iterations'] > 0 else [100],\n",
    "            'tol': [10 ** -details['convergence_tolerance']] if details['convergence_tolerance'] > 0 else [0.1],\n",
    "            'solver': [details['solver'].lower()],\n",
    "            'learning_rate_init': [details['initial_learning_rate']] if details['initial_learning_rate'] > 0 else [0.01],\n",
    "            'shuffle': [details['shuffle_data']],\n",
    "            'batch_size': ['auto'] if details['automatic_batching'] else [details['batch_size']],\n",
    "            'beta_1': [details['beta_1']] if details['beta_1'] != 0 else [.1],\n",
    "            'beta_2': [details['beta_2']] if details['beta_2'] != 0 else [.1],\n",
    "            'epsilon': [details['epsilon']] if details['epsilon'] != 0 else [.1],\n",
    "            'power_t': [details['power_t']] if details['power_t'] != 0 else [.1],\n",
    "            'momentum': [details['momentum']] if details['momentum'] != 0 else [.1],\n",
    "            'nesterovs_momentum': [details['use_nesterov_momentum']]\n",
    "        }\n",
    "        grid_search = GridSearchCV(model, parameters, cv=5)\n",
    "        grid_search.fit(X_train, Y_train)\n",
    "        best_model = grid_search.best_estimator_\n",
    "        Y_pred = best_model.predict(X_test)\n",
    "\n",
    "        confusion_mat = confusion_matrix(Y_test, Y_pred)\n",
    "\n",
    "        # Create heatmap with seaborn\n",
    "        sns.heatmap(confusion_mat, annot=True, cmap=\"viridis\", fmt=\"d\", cbar=False,\n",
    "                    linewidths=0.5, linecolor='gray', square=True,\n",
    "                    xticklabels=True, yticklabels=True, annot_kws={\"size\": 10})\n",
    "        \n",
    "        # Customize axis labels\n",
    "        plt.xlabel(\"Predicted Labels\")\n",
    "        plt.ylabel(\"True Labels\")\n",
    "        # Show plot\n",
    "        plt.show()      \n",
    "        # Assign value to classification_rep_df\n",
    "        classification_rep_dict = classification_report(Y_test, Y_pred, output_dict=True)\n",
    "        classification_rep_df = pd.DataFrame(classification_rep_dict)\n",
    "        # Add some styling to the DataFrame\n",
    "        classification_rep_styled = classification_rep_df.style.background_gradient(cmap='viridis')\n",
    "        # Inside each block where you print the classification report, replace the print statement with the following:\n",
    "        # Print the styled classification report\n",
    "        print(\"Classification Report:\")\n",
    "        display(classification_rep_styled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2f70c7ff-0ee9-4243-ad44-10a7bab28353",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification Report:\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "  background-color: #440154;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_91c36_row3_col0, #T_91c36_row3_col1, #T_91c36_row3_col2, #T_91c36_row3_col4, #T_91c36_row3_col5 {\n",
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       "}\n",
       "</style>\n",
       "<table id=\"T_91c36\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_91c36_level0_col0\" class=\"col_heading level0 col0\" >0</th>\n",
       "      <th id=\"T_91c36_level0_col1\" class=\"col_heading level0 col1\" >1</th>\n",
       "      <th id=\"T_91c36_level0_col2\" class=\"col_heading level0 col2\" >2</th>\n",
       "      <th id=\"T_91c36_level0_col3\" class=\"col_heading level0 col3\" >accuracy</th>\n",
       "      <th id=\"T_91c36_level0_col4\" class=\"col_heading level0 col4\" >macro avg</th>\n",
       "      <th id=\"T_91c36_level0_col5\" class=\"col_heading level0 col5\" >weighted avg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_91c36_level0_row0\" class=\"row_heading level0 row0\" >precision</th>\n",
       "      <td id=\"T_91c36_row0_col0\" class=\"data row0 col0\" >1.000000</td>\n",
       "      <td id=\"T_91c36_row0_col1\" class=\"data row0 col1\" >1.000000</td>\n",
       "      <td id=\"T_91c36_row0_col2\" class=\"data row0 col2\" >1.000000</td>\n",
       "      <td id=\"T_91c36_row0_col3\" class=\"data row0 col3\" >1.000000</td>\n",
       "      <td id=\"T_91c36_row0_col4\" class=\"data row0 col4\" >1.000000</td>\n",
       "      <td id=\"T_91c36_row0_col5\" class=\"data row0 col5\" >1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_91c36_level0_row1\" class=\"row_heading level0 row1\" >recall</th>\n",
       "      <td id=\"T_91c36_row1_col0\" class=\"data row1 col0\" >1.000000</td>\n",
       "      <td id=\"T_91c36_row1_col1\" class=\"data row1 col1\" >1.000000</td>\n",
       "      <td id=\"T_91c36_row1_col2\" class=\"data row1 col2\" >1.000000</td>\n",
       "      <td id=\"T_91c36_row1_col3\" class=\"data row1 col3\" >1.000000</td>\n",
       "      <td id=\"T_91c36_row1_col4\" class=\"data row1 col4\" >1.000000</td>\n",
       "      <td id=\"T_91c36_row1_col5\" class=\"data row1 col5\" >1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_91c36_level0_row2\" class=\"row_heading level0 row2\" >f1-score</th>\n",
       "      <td id=\"T_91c36_row2_col0\" class=\"data row2 col0\" >1.000000</td>\n",
       "      <td id=\"T_91c36_row2_col1\" class=\"data row2 col1\" >1.000000</td>\n",
       "      <td id=\"T_91c36_row2_col2\" class=\"data row2 col2\" >1.000000</td>\n",
       "      <td id=\"T_91c36_row2_col3\" class=\"data row2 col3\" >1.000000</td>\n",
       "      <td id=\"T_91c36_row2_col4\" class=\"data row2 col4\" >1.000000</td>\n",
       "      <td id=\"T_91c36_row2_col5\" class=\"data row2 col5\" >1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_91c36_level0_row3\" class=\"row_heading level0 row3\" >support</th>\n",
       "      <td id=\"T_91c36_row3_col0\" class=\"data row3 col0\" >10.000000</td>\n",
       "      <td id=\"T_91c36_row3_col1\" class=\"data row3 col1\" >13.000000</td>\n",
       "      <td id=\"T_91c36_row3_col2\" class=\"data row3 col2\" >7.000000</td>\n",
       "      <td id=\"T_91c36_row3_col3\" class=\"data row3 col3\" >1.000000</td>\n",
       "      <td id=\"T_91c36_row3_col4\" class=\"data row3 col4\" >30.000000</td>\n",
       "      <td id=\"T_91c36_row3_col5\" class=\"data row3 col5\" >30.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x2d0eaab2890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hackathon_problem(\"DT_Classifier.rtf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "12c25b53-4023-4aca-be05-348fde6000b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_130aa_row0_col0, #T_130aa_row1_col0, #T_130aa_row2_col1 {\n",
       "  text-align: left;\n",
       "}\n",
       "#T_130aa_row0_col1, #T_130aa_row1_col1, #T_130aa_row2_col0 {\n",
       "  text-align: left;\n",
       "  background-color: lightgreen;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_130aa\">\n",
       "  <caption>Model Evaluation Metrics</caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_130aa_level0_col0\" class=\"col_heading level0 col0\" >Metric</th>\n",
       "      <th id=\"T_130aa_level0_col1\" class=\"col_heading level0 col1\" >Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_130aa_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_130aa_row0_col0\" class=\"data row0 col0\" >R-squared</td>\n",
       "      <td id=\"T_130aa_row0_col1\" class=\"data row0 col1\" >1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_130aa_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_130aa_row1_col0\" class=\"data row1 col0\" >Adjusted R-squared</td>\n",
       "      <td id=\"T_130aa_row1_col1\" class=\"data row1 col1\" >1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_130aa_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_130aa_row2_col0\" class=\"data row2 col0\" >Root Mean Squared Error (RMSE)</td>\n",
       "      <td id=\"T_130aa_row2_col1\" class=\"data row2 col1\" >0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x2d0eaa86850>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hackathon_problem(\"DT_Regressor.rtf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "899e54f5-3651-4a98-ae3b-6591bdbfdb30",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('from sklearn.linear_model import ElasticNet'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_5d683_row0_col0, #T_5d683_row1_col0, #T_5d683_row1_col1, #T_5d683_row2_col1 {\n",
       "  text-align: left;\n",
       "}\n",
       "#T_5d683_row0_col1, #T_5d683_row2_col0 {\n",
       "  text-align: left;\n",
       "  background-color: lightgreen;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_5d683\">\n",
       "  <caption>Model Evaluation Metrics</caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_5d683_level0_col0\" class=\"col_heading level0 col0\" >Metric</th>\n",
       "      <th id=\"T_5d683_level0_col1\" class=\"col_heading level0 col1\" >Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_5d683_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_5d683_row0_col0\" class=\"data row0 col0\" >R-squared</td>\n",
       "      <td id=\"T_5d683_row0_col1\" class=\"data row0 col1\" >0.842478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5d683_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_5d683_row1_col0\" class=\"data row1 col0\" >Adjusted R-squared</td>\n",
       "      <td id=\"T_5d683_row1_col1\" class=\"data row1 col1\" >0.817275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5d683_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_5d683_row2_col0\" class=\"data row2 col0\" >Root Mean Squared Error (RMSE)</td>\n",
       "      <td id=\"T_5d683_row2_col1\" class=\"data row2 col1\" >0.296120</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x2d0ea37b290>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hackathon_problem(\"ElasticNetRegression.rtf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c2025fab-4c91-4b2c-bde2-8ebdf68a9a06",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\joblib\\externals\\loky\\backend\\context.py:136: UserWarning: Could not find the number of physical cores for the following reason:\n",
      "[WinError 2] The system cannot find the file specified\n",
      "Returning the number of logical cores instead. You can silence this warning by setting LOKY_MAX_CPU_COUNT to the number of cores you want to use.\n",
      "  warnings.warn(\n",
      "  File \"C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\joblib\\externals\\loky\\backend\\context.py\", line 257, in _count_physical_cores\n",
      "    cpu_info = subprocess.run(\n",
      "               ^^^^^^^^^^^^^^^\n",
      "  File \"C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\subprocess.py\", line 548, in run\n",
      "    with Popen(*popenargs, **kwargs) as process:\n",
      "         ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\subprocess.py\", line 1026, in __init__\n",
      "    self._execute_child(args, executable, preexec_fn, close_fds,\n",
      "  File \"C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\subprocess.py\", line 1538, in _execute_child\n",
      "    hp, ht, pid, tid = _winapi.CreateProcess(executable, args,\n",
      "                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification Report:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_00ee1_row0_col0, #T_00ee1_row0_col1, #T_00ee1_row0_col2, #T_00ee1_row0_col3, #T_00ee1_row0_col4, #T_00ee1_row0_col5, #T_00ee1_row1_col0, #T_00ee1_row1_col1, #T_00ee1_row1_col2, #T_00ee1_row1_col3, #T_00ee1_row1_col4, #T_00ee1_row1_col5, #T_00ee1_row2_col0, #T_00ee1_row2_col1, #T_00ee1_row2_col2, #T_00ee1_row2_col3, #T_00ee1_row2_col4, #T_00ee1_row2_col5, #T_00ee1_row3_col3 {\n",
       "  background-color: #440154;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_00ee1_row3_col0, #T_00ee1_row3_col1, #T_00ee1_row3_col2, #T_00ee1_row3_col4, #T_00ee1_row3_col5 {\n",
       "  background-color: #fde725;\n",
       "  color: #000000;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_00ee1\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_00ee1_level0_col0\" class=\"col_heading level0 col0\" >0</th>\n",
       "      <th id=\"T_00ee1_level0_col1\" class=\"col_heading level0 col1\" >1</th>\n",
       "      <th id=\"T_00ee1_level0_col2\" class=\"col_heading level0 col2\" >2</th>\n",
       "      <th id=\"T_00ee1_level0_col3\" class=\"col_heading level0 col3\" >accuracy</th>\n",
       "      <th id=\"T_00ee1_level0_col4\" class=\"col_heading level0 col4\" >macro avg</th>\n",
       "      <th id=\"T_00ee1_level0_col5\" class=\"col_heading level0 col5\" >weighted avg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_00ee1_level0_row0\" class=\"row_heading level0 row0\" >precision</th>\n",
       "      <td id=\"T_00ee1_row0_col0\" class=\"data row0 col0\" >1.000000</td>\n",
       "      <td id=\"T_00ee1_row0_col1\" class=\"data row0 col1\" >1.000000</td>\n",
       "      <td id=\"T_00ee1_row0_col2\" class=\"data row0 col2\" >1.000000</td>\n",
       "      <td id=\"T_00ee1_row0_col3\" class=\"data row0 col3\" >1.000000</td>\n",
       "      <td id=\"T_00ee1_row0_col4\" class=\"data row0 col4\" >1.000000</td>\n",
       "      <td id=\"T_00ee1_row0_col5\" class=\"data row0 col5\" >1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_00ee1_level0_row1\" class=\"row_heading level0 row1\" >recall</th>\n",
       "      <td id=\"T_00ee1_row1_col0\" class=\"data row1 col0\" >1.000000</td>\n",
       "      <td id=\"T_00ee1_row1_col1\" class=\"data row1 col1\" >1.000000</td>\n",
       "      <td id=\"T_00ee1_row1_col2\" class=\"data row1 col2\" >1.000000</td>\n",
       "      <td id=\"T_00ee1_row1_col3\" class=\"data row1 col3\" >1.000000</td>\n",
       "      <td id=\"T_00ee1_row1_col4\" class=\"data row1 col4\" >1.000000</td>\n",
       "      <td id=\"T_00ee1_row1_col5\" class=\"data row1 col5\" >1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_00ee1_level0_row2\" class=\"row_heading level0 row2\" >f1-score</th>\n",
       "      <td id=\"T_00ee1_row2_col0\" class=\"data row2 col0\" >1.000000</td>\n",
       "      <td id=\"T_00ee1_row2_col1\" class=\"data row2 col1\" >1.000000</td>\n",
       "      <td id=\"T_00ee1_row2_col2\" class=\"data row2 col2\" >1.000000</td>\n",
       "      <td id=\"T_00ee1_row2_col3\" class=\"data row2 col3\" >1.000000</td>\n",
       "      <td id=\"T_00ee1_row2_col4\" class=\"data row2 col4\" >1.000000</td>\n",
       "      <td id=\"T_00ee1_row2_col5\" class=\"data row2 col5\" >1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_00ee1_level0_row3\" class=\"row_heading level0 row3\" >support</th>\n",
       "      <td id=\"T_00ee1_row3_col0\" class=\"data row3 col0\" >10.000000</td>\n",
       "      <td id=\"T_00ee1_row3_col1\" class=\"data row3 col1\" >13.000000</td>\n",
       "      <td id=\"T_00ee1_row3_col2\" class=\"data row3 col2\" >7.000000</td>\n",
       "      <td id=\"T_00ee1_row3_col3\" class=\"data row3 col3\" >1.000000</td>\n",
       "      <td id=\"T_00ee1_row3_col4\" class=\"data row3 col4\" >30.000000</td>\n",
       "      <td id=\"T_00ee1_row3_col5\" class=\"data row3 col5\" >30.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x2d0eaa49750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hackathon_problem(\"KNN.rtf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e43d09d6-fa80-4ce5-81bd-6b418a8bc02b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('from sklearn.linear_model import Lasso\\nfrom sklearn.linear_model import ElasticNet'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_9b49e_row0_col0, #T_9b49e_row1_col0, #T_9b49e_row1_col1, #T_9b49e_row2_col1 {\n",
       "  text-align: left;\n",
       "}\n",
       "#T_9b49e_row0_col1, #T_9b49e_row2_col0 {\n",
       "  text-align: left;\n",
       "  background-color: lightgreen;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_9b49e\">\n",
       "  <caption>Model Evaluation Metrics</caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_9b49e_level0_col0\" class=\"col_heading level0 col0\" >Metric</th>\n",
       "      <th id=\"T_9b49e_level0_col1\" class=\"col_heading level0 col1\" >Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_9b49e_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_9b49e_row0_col0\" class=\"data row0 col0\" >R-squared</td>\n",
       "      <td id=\"T_9b49e_row0_col1\" class=\"data row0 col1\" >0.784338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9b49e_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_9b49e_row1_col0\" class=\"data row1 col0\" >Adjusted R-squared</td>\n",
       "      <td id=\"T_9b49e_row1_col1\" class=\"data row1 col1\" >0.749832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9b49e_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_9b49e_row2_col0\" class=\"data row2 col0\" >Root Mean Squared Error (RMSE)</td>\n",
       "      <td id=\"T_9b49e_row2_col1\" class=\"data row2 col1\" >0.346485</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x2d0fcc42b90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hackathon_problem(\"LassoRegression.rtf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2a314e79-5fbc-4c87-a298-11c935fd6f79",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_9a7be_row0_col0, #T_9a7be_row1_col0, #T_9a7be_row2_col1 {\n",
       "  text-align: left;\n",
       "}\n",
       "#T_9a7be_row0_col1, #T_9a7be_row1_col1, #T_9a7be_row2_col0 {\n",
       "  text-align: left;\n",
       "  background-color: lightgreen;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_9a7be\">\n",
       "  <caption>Model Evaluation Metrics</caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_9a7be_level0_col0\" class=\"col_heading level0 col0\" >Metric</th>\n",
       "      <th id=\"T_9a7be_level0_col1\" class=\"col_heading level0 col1\" >Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_9a7be_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_9a7be_row0_col0\" class=\"data row0 col0\" >R-squared</td>\n",
       "      <td id=\"T_9a7be_row0_col1\" class=\"data row0 col1\" >1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9a7be_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_9a7be_row1_col0\" class=\"data row1 col0\" >Adjusted R-squared</td>\n",
       "      <td id=\"T_9a7be_row1_col1\" class=\"data row1 col1\" >1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9a7be_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_9a7be_row2_col0\" class=\"data row2 col0\" >Root Mean Squared Error (RMSE)</td>\n",
       "      <td id=\"T_9a7be_row2_col1\" class=\"data row2 col1\" >0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x2d0eab32f90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hackathon_problem(\"Linear.rtf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "60675155-26b7-4f5a-b7b9-e48633c0f17c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('from sklearn.linear_model import Lasso\\nfrom sklearn.linear_model import LogisticRegression\\nfrom sklearn.linear_model import ElasticNet'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:1175: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification Report:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_9959f_row0_col0, #T_9959f_row0_col1, #T_9959f_row0_col2, #T_9959f_row0_col3, #T_9959f_row0_col4, #T_9959f_row0_col5, #T_9959f_row1_col0, #T_9959f_row1_col1, #T_9959f_row1_col2, #T_9959f_row1_col3, #T_9959f_row1_col4, #T_9959f_row1_col5, #T_9959f_row2_col0, #T_9959f_row2_col1, #T_9959f_row2_col2, #T_9959f_row2_col3, #T_9959f_row2_col4, #T_9959f_row2_col5, #T_9959f_row3_col3 {\n",
       "  background-color: #440154;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_9959f_row3_col0, #T_9959f_row3_col1, #T_9959f_row3_col2, #T_9959f_row3_col4, #T_9959f_row3_col5 {\n",
       "  background-color: #fde725;\n",
       "  color: #000000;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_9959f\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_9959f_level0_col0\" class=\"col_heading level0 col0\" >0</th>\n",
       "      <th id=\"T_9959f_level0_col1\" class=\"col_heading level0 col1\" >1</th>\n",
       "      <th id=\"T_9959f_level0_col2\" class=\"col_heading level0 col2\" >2</th>\n",
       "      <th id=\"T_9959f_level0_col3\" class=\"col_heading level0 col3\" >accuracy</th>\n",
       "      <th id=\"T_9959f_level0_col4\" class=\"col_heading level0 col4\" >macro avg</th>\n",
       "      <th id=\"T_9959f_level0_col5\" class=\"col_heading level0 col5\" >weighted avg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_9959f_level0_row0\" class=\"row_heading level0 row0\" >precision</th>\n",
       "      <td id=\"T_9959f_row0_col0\" class=\"data row0 col0\" >1.000000</td>\n",
       "      <td id=\"T_9959f_row0_col1\" class=\"data row0 col1\" >1.000000</td>\n",
       "      <td id=\"T_9959f_row0_col2\" class=\"data row0 col2\" >1.000000</td>\n",
       "      <td id=\"T_9959f_row0_col3\" class=\"data row0 col3\" >1.000000</td>\n",
       "      <td id=\"T_9959f_row0_col4\" class=\"data row0 col4\" >1.000000</td>\n",
       "      <td id=\"T_9959f_row0_col5\" class=\"data row0 col5\" >1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9959f_level0_row1\" class=\"row_heading level0 row1\" >recall</th>\n",
       "      <td id=\"T_9959f_row1_col0\" class=\"data row1 col0\" >1.000000</td>\n",
       "      <td id=\"T_9959f_row1_col1\" class=\"data row1 col1\" >1.000000</td>\n",
       "      <td id=\"T_9959f_row1_col2\" class=\"data row1 col2\" >1.000000</td>\n",
       "      <td id=\"T_9959f_row1_col3\" class=\"data row1 col3\" >1.000000</td>\n",
       "      <td id=\"T_9959f_row1_col4\" class=\"data row1 col4\" >1.000000</td>\n",
       "      <td id=\"T_9959f_row1_col5\" class=\"data row1 col5\" >1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9959f_level0_row2\" class=\"row_heading level0 row2\" >f1-score</th>\n",
       "      <td id=\"T_9959f_row2_col0\" class=\"data row2 col0\" >1.000000</td>\n",
       "      <td id=\"T_9959f_row2_col1\" class=\"data row2 col1\" >1.000000</td>\n",
       "      <td id=\"T_9959f_row2_col2\" class=\"data row2 col2\" >1.000000</td>\n",
       "      <td id=\"T_9959f_row2_col3\" class=\"data row2 col3\" >1.000000</td>\n",
       "      <td id=\"T_9959f_row2_col4\" class=\"data row2 col4\" >1.000000</td>\n",
       "      <td id=\"T_9959f_row2_col5\" class=\"data row2 col5\" >1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9959f_level0_row3\" class=\"row_heading level0 row3\" >support</th>\n",
       "      <td id=\"T_9959f_row3_col0\" class=\"data row3 col0\" >10.000000</td>\n",
       "      <td id=\"T_9959f_row3_col1\" class=\"data row3 col1\" >13.000000</td>\n",
       "      <td id=\"T_9959f_row3_col2\" class=\"data row3 col2\" >7.000000</td>\n",
       "      <td id=\"T_9959f_row3_col3\" class=\"data row3 col3\" >1.000000</td>\n",
       "      <td id=\"T_9959f_row3_col4\" class=\"data row3 col4\" >30.000000</td>\n",
       "      <td id=\"T_9959f_row3_col5\" class=\"data row3 col5\" >30.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x2d0eaa86910>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hackathon_problem(\"LogisticRegression.rtf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f39eff6f-4c61-41cc-afab-1a1839bdee77",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification Report:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_73d79_row0_col0, #T_73d79_row0_col2, #T_73d79_row0_col3, #T_73d79_row0_col4, #T_73d79_row0_col5, #T_73d79_row1_col0, #T_73d79_row1_col1, #T_73d79_row1_col3, #T_73d79_row1_col4, #T_73d79_row1_col5, #T_73d79_row2_col0, #T_73d79_row2_col3, #T_73d79_row2_col4, #T_73d79_row2_col5, #T_73d79_row3_col3 {\n",
       "  background-color: #440154;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_73d79_row0_col1, #T_73d79_row2_col2 {\n",
       "  background-color: #46075a;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_73d79_row1_col2 {\n",
       "  background-color: #471063;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_73d79_row2_col1 {\n",
       "  background-color: #440256;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_73d79_row3_col0, #T_73d79_row3_col1, #T_73d79_row3_col2, #T_73d79_row3_col4, #T_73d79_row3_col5 {\n",
       "  background-color: #fde725;\n",
       "  color: #000000;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_73d79\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_73d79_level0_col0\" class=\"col_heading level0 col0\" >0</th>\n",
       "      <th id=\"T_73d79_level0_col1\" class=\"col_heading level0 col1\" >1</th>\n",
       "      <th id=\"T_73d79_level0_col2\" class=\"col_heading level0 col2\" >2</th>\n",
       "      <th id=\"T_73d79_level0_col3\" class=\"col_heading level0 col3\" >accuracy</th>\n",
       "      <th id=\"T_73d79_level0_col4\" class=\"col_heading level0 col4\" >macro avg</th>\n",
       "      <th id=\"T_73d79_level0_col5\" class=\"col_heading level0 col5\" >weighted avg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_73d79_level0_row0\" class=\"row_heading level0 row0\" >precision</th>\n",
       "      <td id=\"T_73d79_row0_col0\" class=\"data row0 col0\" >1.000000</td>\n",
       "      <td id=\"T_73d79_row0_col1\" class=\"data row0 col1\" >0.900000</td>\n",
       "      <td id=\"T_73d79_row0_col2\" class=\"data row0 col2\" >0.600000</td>\n",
       "      <td id=\"T_73d79_row0_col3\" class=\"data row0 col3\" >0.833333</td>\n",
       "      <td id=\"T_73d79_row0_col4\" class=\"data row0 col4\" >0.833333</td>\n",
       "      <td id=\"T_73d79_row0_col5\" class=\"data row0 col5\" >0.863333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_73d79_level0_row1\" class=\"row_heading level0 row1\" >recall</th>\n",
       "      <td id=\"T_73d79_row1_col0\" class=\"data row1 col0\" >1.000000</td>\n",
       "      <td id=\"T_73d79_row1_col1\" class=\"data row1 col1\" >0.692308</td>\n",
       "      <td id=\"T_73d79_row1_col2\" class=\"data row1 col2\" >0.857143</td>\n",
       "      <td id=\"T_73d79_row1_col3\" class=\"data row1 col3\" >0.833333</td>\n",
       "      <td id=\"T_73d79_row1_col4\" class=\"data row1 col4\" >0.849817</td>\n",
       "      <td id=\"T_73d79_row1_col5\" class=\"data row1 col5\" >0.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_73d79_level0_row2\" class=\"row_heading level0 row2\" >f1-score</th>\n",
       "      <td id=\"T_73d79_row2_col0\" class=\"data row2 col0\" >1.000000</td>\n",
       "      <td id=\"T_73d79_row2_col1\" class=\"data row2 col1\" >0.782609</td>\n",
       "      <td id=\"T_73d79_row2_col2\" class=\"data row2 col2\" >0.705882</td>\n",
       "      <td id=\"T_73d79_row2_col3\" class=\"data row2 col3\" >0.833333</td>\n",
       "      <td id=\"T_73d79_row2_col4\" class=\"data row2 col4\" >0.829497</td>\n",
       "      <td id=\"T_73d79_row2_col5\" class=\"data row2 col5\" >0.837170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_73d79_level0_row3\" class=\"row_heading level0 row3\" >support</th>\n",
       "      <td id=\"T_73d79_row3_col0\" class=\"data row3 col0\" >10.000000</td>\n",
       "      <td id=\"T_73d79_row3_col1\" class=\"data row3 col1\" >13.000000</td>\n",
       "      <td id=\"T_73d79_row3_col2\" class=\"data row3 col2\" >7.000000</td>\n",
       "      <td id=\"T_73d79_row3_col3\" class=\"data row3 col3\" >0.833333</td>\n",
       "      <td id=\"T_73d79_row3_col4\" class=\"data row3 col4\" >30.000000</td>\n",
       "      <td id=\"T_73d79_row3_col5\" class=\"data row3 col5\" >30.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x2d0ffdfe5d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hackathon_problem(\"Neural_Network.rtf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2da0ab18-13d7-4cee-a71c-ba427db69689",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification Report:\n"
     ]
    },
    {
     "data": {
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       "#T_1d834_row3_col0, #T_1d834_row3_col1, #T_1d834_row3_col2, #T_1d834_row3_col4, #T_1d834_row3_col5 {\n",
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       "</style>\n",
       "<table id=\"T_1d834\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_1d834_level0_col0\" class=\"col_heading level0 col0\" >0</th>\n",
       "      <th id=\"T_1d834_level0_col1\" class=\"col_heading level0 col1\" >1</th>\n",
       "      <th id=\"T_1d834_level0_col2\" class=\"col_heading level0 col2\" >2</th>\n",
       "      <th id=\"T_1d834_level0_col3\" class=\"col_heading level0 col3\" >accuracy</th>\n",
       "      <th id=\"T_1d834_level0_col4\" class=\"col_heading level0 col4\" >macro avg</th>\n",
       "      <th id=\"T_1d834_level0_col5\" class=\"col_heading level0 col5\" >weighted avg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_1d834_level0_row0\" class=\"row_heading level0 row0\" >precision</th>\n",
       "      <td id=\"T_1d834_row0_col0\" class=\"data row0 col0\" >1.000000</td>\n",
       "      <td id=\"T_1d834_row0_col1\" class=\"data row0 col1\" >1.000000</td>\n",
       "      <td id=\"T_1d834_row0_col2\" class=\"data row0 col2\" >1.000000</td>\n",
       "      <td id=\"T_1d834_row0_col3\" class=\"data row0 col3\" >1.000000</td>\n",
       "      <td id=\"T_1d834_row0_col4\" class=\"data row0 col4\" >1.000000</td>\n",
       "      <td id=\"T_1d834_row0_col5\" class=\"data row0 col5\" >1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1d834_level0_row1\" class=\"row_heading level0 row1\" >recall</th>\n",
       "      <td id=\"T_1d834_row1_col0\" class=\"data row1 col0\" >1.000000</td>\n",
       "      <td id=\"T_1d834_row1_col1\" class=\"data row1 col1\" >1.000000</td>\n",
       "      <td id=\"T_1d834_row1_col2\" class=\"data row1 col2\" >1.000000</td>\n",
       "      <td id=\"T_1d834_row1_col3\" class=\"data row1 col3\" >1.000000</td>\n",
       "      <td id=\"T_1d834_row1_col4\" class=\"data row1 col4\" >1.000000</td>\n",
       "      <td id=\"T_1d834_row1_col5\" class=\"data row1 col5\" >1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1d834_level0_row2\" class=\"row_heading level0 row2\" >f1-score</th>\n",
       "      <td id=\"T_1d834_row2_col0\" class=\"data row2 col0\" >1.000000</td>\n",
       "      <td id=\"T_1d834_row2_col1\" class=\"data row2 col1\" >1.000000</td>\n",
       "      <td id=\"T_1d834_row2_col2\" class=\"data row2 col2\" >1.000000</td>\n",
       "      <td id=\"T_1d834_row2_col3\" class=\"data row2 col3\" >1.000000</td>\n",
       "      <td id=\"T_1d834_row2_col4\" class=\"data row2 col4\" >1.000000</td>\n",
       "      <td id=\"T_1d834_row2_col5\" class=\"data row2 col5\" >1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1d834_level0_row3\" class=\"row_heading level0 row3\" >support</th>\n",
       "      <td id=\"T_1d834_row3_col0\" class=\"data row3 col0\" >10.000000</td>\n",
       "      <td id=\"T_1d834_row3_col1\" class=\"data row3 col1\" >13.000000</td>\n",
       "      <td id=\"T_1d834_row3_col2\" class=\"data row3 col2\" >7.000000</td>\n",
       "      <td id=\"T_1d834_row3_col3\" class=\"data row3 col3\" >1.000000</td>\n",
       "      <td id=\"T_1d834_row3_col4\" class=\"data row3 col4\" >30.000000</td>\n",
       "      <td id=\"T_1d834_row3_col5\" class=\"data row3 col5\" >30.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x2d0fca88f10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hackathon_problem(\"RF_Classifier.rtf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "516482be-5188-44dc-9758-f0abc5adfd06",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_a5078_row0_col0, #T_a5078_row1_col0, #T_a5078_row2_col1 {\n",
       "  text-align: left;\n",
       "}\n",
       "#T_a5078_row0_col1, #T_a5078_row1_col1, #T_a5078_row2_col0 {\n",
       "  text-align: left;\n",
       "  background-color: lightgreen;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_a5078\">\n",
       "  <caption>Model Evaluation Metrics</caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_a5078_level0_col0\" class=\"col_heading level0 col0\" >Metric</th>\n",
       "      <th id=\"T_a5078_level0_col1\" class=\"col_heading level0 col1\" >Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_a5078_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_a5078_row0_col0\" class=\"data row0 col0\" >R-squared</td>\n",
       "      <td id=\"T_a5078_row0_col1\" class=\"data row0 col1\" >1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a5078_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_a5078_row1_col0\" class=\"data row1 col0\" >Adjusted R-squared</td>\n",
       "      <td id=\"T_a5078_row1_col1\" class=\"data row1 col1\" >1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a5078_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_a5078_row2_col0\" class=\"data row2 col0\" >Root Mean Squared Error (RMSE)</td>\n",
       "      <td id=\"T_a5078_row2_col1\" class=\"data row2 col1\" >0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x2d0eaaf8f90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hackathon_problem(\"RF_Regressor.rtf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8af2ad4d-361c-4b86-9e0f-5f38f38db1ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('from sklearn.linear_model import Lasso\\nfrom sklearn.linear_model import LogisticRegression\\nfrom sklearn.linear_model import Ridge\\nfrom sklearn.linear_model import ElasticNet'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_91244_row0_col0, #T_91244_row1_col0, #T_91244_row1_col1, #T_91244_row2_col1 {\n",
       "  text-align: left;\n",
       "}\n",
       "#T_91244_row0_col1, #T_91244_row2_col0 {\n",
       "  text-align: left;\n",
       "  background-color: lightgreen;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_91244\">\n",
       "  <caption>Model Evaluation Metrics</caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_91244_level0_col0\" class=\"col_heading level0 col0\" >Metric</th>\n",
       "      <th id=\"T_91244_level0_col1\" class=\"col_heading level0 col1\" >Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_91244_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_91244_row0_col0\" class=\"data row0 col0\" >R-squared</td>\n",
       "      <td id=\"T_91244_row0_col1\" class=\"data row0 col1\" >0.999387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_91244_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_91244_row1_col0\" class=\"data row1 col0\" >Adjusted R-squared</td>\n",
       "      <td id=\"T_91244_row1_col1\" class=\"data row1 col1\" >0.999289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_91244_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_91244_row2_col0\" class=\"data row2 col0\" >Root Mean Squared Error (RMSE)</td>\n",
       "      <td id=\"T_91244_row2_col1\" class=\"data row2 col1\" >0.018470</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x2d0eaaf3b90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hackathon_problem(\"RidgeRegression.rtf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c3b37933-7d0c-40aa-a6e6-12c573cba030",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification Report:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_60a02_row0_col0, #T_60a02_row0_col1, #T_60a02_row0_col2, #T_60a02_row0_col3, #T_60a02_row0_col4, #T_60a02_row0_col5, #T_60a02_row1_col0, #T_60a02_row1_col1, #T_60a02_row1_col2, #T_60a02_row1_col3, #T_60a02_row1_col4, #T_60a02_row1_col5, #T_60a02_row2_col0, #T_60a02_row2_col1, #T_60a02_row2_col2, #T_60a02_row2_col3, #T_60a02_row2_col4, #T_60a02_row2_col5, #T_60a02_row3_col3 {\n",
       "  background-color: #440154;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_60a02_row3_col0, #T_60a02_row3_col1, #T_60a02_row3_col2, #T_60a02_row3_col4, #T_60a02_row3_col5 {\n",
       "  background-color: #fde725;\n",
       "  color: #000000;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_60a02\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_60a02_level0_col0\" class=\"col_heading level0 col0\" >0</th>\n",
       "      <th id=\"T_60a02_level0_col1\" class=\"col_heading level0 col1\" >1</th>\n",
       "      <th id=\"T_60a02_level0_col2\" class=\"col_heading level0 col2\" >2</th>\n",
       "      <th id=\"T_60a02_level0_col3\" class=\"col_heading level0 col3\" >accuracy</th>\n",
       "      <th id=\"T_60a02_level0_col4\" class=\"col_heading level0 col4\" >macro avg</th>\n",
       "      <th id=\"T_60a02_level0_col5\" class=\"col_heading level0 col5\" >weighted avg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_60a02_level0_row0\" class=\"row_heading level0 row0\" >precision</th>\n",
       "      <td id=\"T_60a02_row0_col0\" class=\"data row0 col0\" >1.000000</td>\n",
       "      <td id=\"T_60a02_row0_col1\" class=\"data row0 col1\" >1.000000</td>\n",
       "      <td id=\"T_60a02_row0_col2\" class=\"data row0 col2\" >1.000000</td>\n",
       "      <td id=\"T_60a02_row0_col3\" class=\"data row0 col3\" >1.000000</td>\n",
       "      <td id=\"T_60a02_row0_col4\" class=\"data row0 col4\" >1.000000</td>\n",
       "      <td id=\"T_60a02_row0_col5\" class=\"data row0 col5\" >1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_60a02_level0_row1\" class=\"row_heading level0 row1\" >recall</th>\n",
       "      <td id=\"T_60a02_row1_col0\" class=\"data row1 col0\" >1.000000</td>\n",
       "      <td id=\"T_60a02_row1_col1\" class=\"data row1 col1\" >1.000000</td>\n",
       "      <td id=\"T_60a02_row1_col2\" class=\"data row1 col2\" >1.000000</td>\n",
       "      <td id=\"T_60a02_row1_col3\" class=\"data row1 col3\" >1.000000</td>\n",
       "      <td id=\"T_60a02_row1_col4\" class=\"data row1 col4\" >1.000000</td>\n",
       "      <td id=\"T_60a02_row1_col5\" class=\"data row1 col5\" >1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_60a02_level0_row2\" class=\"row_heading level0 row2\" >f1-score</th>\n",
       "      <td id=\"T_60a02_row2_col0\" class=\"data row2 col0\" >1.000000</td>\n",
       "      <td id=\"T_60a02_row2_col1\" class=\"data row2 col1\" >1.000000</td>\n",
       "      <td id=\"T_60a02_row2_col2\" class=\"data row2 col2\" >1.000000</td>\n",
       "      <td id=\"T_60a02_row2_col3\" class=\"data row2 col3\" >1.000000</td>\n",
       "      <td id=\"T_60a02_row2_col4\" class=\"data row2 col4\" >1.000000</td>\n",
       "      <td id=\"T_60a02_row2_col5\" class=\"data row2 col5\" >1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_60a02_level0_row3\" class=\"row_heading level0 row3\" >support</th>\n",
       "      <td id=\"T_60a02_row3_col0\" class=\"data row3 col0\" >10.000000</td>\n",
       "      <td id=\"T_60a02_row3_col1\" class=\"data row3 col1\" >13.000000</td>\n",
       "      <td id=\"T_60a02_row3_col2\" class=\"data row3 col2\" >7.000000</td>\n",
       "      <td id=\"T_60a02_row3_col3\" class=\"data row3 col3\" >1.000000</td>\n",
       "      <td id=\"T_60a02_row3_col4\" class=\"data row3 col4\" >30.000000</td>\n",
       "      <td id=\"T_60a02_row3_col5\" class=\"data row3 col5\" >30.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x2d0fcc2c1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hackathon_problem(\"SVM.rtf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4cd610f9-8184-4353-953b-fa6a2a6e79c2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\tvalidation_0-mlogloss:0.99140\n",
      "[1]\tvalidation_0-mlogloss:0.93887\n",
      "[2]\tvalidation_0-mlogloss:0.84715\n",
      "[3]\tvalidation_0-mlogloss:0.76654\n",
      "[4]\tvalidation_0-mlogloss:0.70373\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\xgboost\\sklearn.py:889: UserWarning: `early_stopping_rounds` in `fit` method is deprecated for better compatibility with scikit-learn, use `early_stopping_rounds` in constructor or`set_params` instead.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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rdl2nVr61xu7RcJlyzknffPvLRZK+z/jlz/8+IZ3Llaa/Ie3+WvrhuPR1uvTMS9KJ01LczbaODS/htV0xt3dVLjxMd6FTp055/OCdOnXSzp07NXDgwIuur2jvDNK69zcpNCJY8Sl3KywyVAd3H9H//O4FZZ7MqviHUaV9nS7FT/jPEYj/O/uXPw/qZ2lKonToqLRspXQmSwoNlmKuld6e+csp9TAfr+2KuR2wL7/8ssLb9OxZxi+slOGJJ55QTk5Omeujo6O1Zg3/2qjIR7M/5bDCFei3HaVv1pX9D7jXnq/EYWALXtvlcztgvghJjx49yl1ft25dxcbGev1xAQDm4/vAAABGImAAACMRMACAkQgYAMBIBAwAYKRLCtiGDRs0dOhQde3aVT/88IMkadGiRdq4caNXhwMAoCweB2zJkiWKi4tTQECAvvzyS+Xl5UmSsrKyvP5p9AAAlMXjgD3//POaO3eu5s2bp1q1armWd+/enY9+AgBUGo8Dlp6eftFP3AgJCVFmZqY3ZgIAoEIeBywyMlIHDhwotXzjxo1q0aKFV4YCAKAiHgfs4Ycf1vjx47V161Y5HA79+9//1uLFizVp0iSNGTPGFzMCAFCKx1+c9fTTT6u4uFi9e/fWuXPn1LNnTzmdTk2aNEnjxo3zxYwAAJTiccAcDoeeeeYZPfHEEzpw4ICys7PVtm1bBQYG+mI+AAAu6pK/utjf319t27b15iwAALjN44D16tVLDoejzPVffPHFZQ0EAIA7PA5Yhw4dSlwvKCjQ7t279dVXXyk+Pt5bcwEAUC6PAzZ9+vSLLp8yZYqys7MveyAAANzhtQ/zHTp0qObPn++tuwMAoFxeC9jmzZtVu3Ztb90dAADl8vgQ4p133lniumVZysjI0I4dOzR58mSvDQYAQHk8DlhISEiJ6zVq1FDr1q01depU3XrrrV4bDACA8ngUsKKiIo0YMUIxMTEKCwvz1UwAAFTIo/fAatasqVtvvZVPnQcA2M7jkzjatWunQ4cO+WIWAADcdklfaDlp0iSlpaUpIyNDZ8+eLXEBAKAyuP0e2NSpU/X444+rf//+kqTbb7+9xEdKWZYlh8OhoqIi708JAMAF3A5YSkqKRo8erTVr1vhyHgAA3OJ2wCzLkiTFxsb6bBgAANzl0Xtg5X0KPQAAlcmj3wNr1apVhRH76aefLmsgAADc4VHAUlJSSn0SBwAAdvAoYPfcc48aNGjgq1kAAHCb2++B8f4XAKAqcTtgv56FCABAVeD2IcTi4mJfzgEAgEe89oWWAABUJgIGADASAQMAGImAAQCMRMAAAEYiYAAAIxEwAICRCBgAwEgEDABgJAIGADASAQMAGImAAQCMRMAAAEYiYAAAIxEwAICRCBgAwEgEDABgJAIGADASAQMAGImAAQCM5LAsy7J7CG9LSUmxewQAwGVITk6u8DZ+lTCHLTamfGX3CKgENyW3Y1tXIzclt9NbUXXsHgOVoOJ8cQgRAGAoAgYAMBIBAwAYiYABAIxEwAAARiJgAAAjETAAgJEIGADASAQMAGAkAgYAMBIBAwAYiYABAIxEwAAARiJgAAAjETAAgJEIGADASAQMAGAkAgYAMBIBAwAYiYABAIxEwAAARiJgAAAjETAAgJEIGADASAQMAGAkAgYAMBIBAwAYiYABAIxEwAAARiJgAAAjETAAgJEIGADASAQMAGAkAgYAMBIBAwAYiYABAIxEwAAARiJgAAAjETAAgJEIGADASAQMAGAkAgYAMBIBAwAYiYABAIxEwAAARiJgAAAjETAAgJEIGADASAQMAGAkAgYAMBIBAwAYiYABAIxEwAAARiJgAAAj+dk9AC7f7Y/G6a5Jt6teZKgO7vlOsx+br/TtB+weCz7C9r7ybbx3lBoHhZRa/pevv9Rz//jchomqJgJmuNgh3fTItHjNHPOGvtl6QHdO+L1SP31GD147Xpmnzto9HryM7V093L50kWo6/nOArFW9cC3+/RCtOJRu41RVD4cQDTd44m36+5urtXLBWh395nu9OvoN5Z3LV9yDt9g9GnyA7V09/HQ+V6dyc1yX3k1b6EjWGW3JOGb3aFUKATOYXy0/terUQrs+3+taZlmWdn2+V21vbGXjZPAFtnf1VKtGDQ1q2Vbvp//T7lGqHNsDlpubq40bN2rfvn2l1p0/f15/+ctfyv35vLw8nT17tsSlsLDQV+NWKSHhQarpV1NnTmSVWH7mZJbCIkPtGQo+w/aunm5t3lLB/rX1t/1f2T1KlWNrwPbv3682bdqoZ8+eiomJUWxsrDIyMlzrs7KyNGLEiHLvIzU1VSEhISUuGzZs8PXoAFAp7m4do7XHDunkuRy7R6lybA3YU089pXbt2unkyZNKT09XUFCQunfvrqNHj7p9H0lJScrKyipx6dGjhw+nrjqyTv+sosIihTUsebZSWIMQnTmeac9Q8Bm2d/VzVWCwul/VTO/9i8OHF2NrwDZt2qTU1FSFh4crOjpan3zyieLi4tSjRw8dOnTIrftwOp0KDg4ucfHzqx4nVxYWFGr/zkPq2DvGtczhcKhj7xjt27LfxsngC2zv6ueu1u304/lz+uLoQbtHqZJsDVhubm6J2DgcDs2ZM0cDBgxQbGys9u/nRVmRJdPT1H9kb/UdFqum116lx+Y8rNp1nVr51hq7R4MPsL2rD4ek/9OqnZbs/1pFlmX3OFWSrbsq1157rXbs2KE2bdqUWD5r1ixJ0u23327HWEZZ9/4mhUYEKz7lboVFhurg7iP6n9+9oMyTWRX/MIzD9q4+brqquRoHhXD2YTlsDdgdd9yhd999Vw888ECpdbNmzVJxcbHmzp1rw2Rm+Wj2p/po9qd2j4FKwvauHjb8cETN33jF7jGqNFsPISYlJWnFihVlrn/99ddVXFxciRMBAExh+++BAQBwKQgYAMBIBAwAYCQCBgAwEgEDABiJgAEAjETAAABGImAAACMRMACAkQgYAMBIBAwAYCQCBgAwEgEDABiJgAEAjETAAABGImAAACMRMACAkQgYAMBIBAwAYCQCBgAwEgEDABiJgAEAjETAAABGImAAACMRMACAkQgYAMBIBAwAYCQCBgAwEgEDABiJgAEAjETAAABGImAAACMRMACAkQgYAMBIBAwAYCQCBgAwEgEDABiJgAEAjETAAABGImAAACMRMACAkQgYAMBIBAwAYCQCBgAwEgEDABiJgAEAjETAAABGImAAACMRMACAkQgYAMBIBAwAYCQCBgAwEgEDABiJgAEAjETAAABGcliWZdk9BC5fXl6eUlNTlZSUJKfTafc48CG2dfXBti4fAbtCnD17ViEhIcrKylJwcLDd48CH2NbVB9u6fBxCBAAYiYABAIxEwAAARiJgVwin06nk5GTe6K0G2NbVB9u6fJzEAQAwEntgAAAjETAAgJEIGADASAQMAGAkAnYFmD17tpo3b67atWurS5cu2rZtm90jwQfWr1+vAQMGqFGjRnI4HFq2bJndI8FHUlNTdcMNNygoKEgNGjTQoEGDlJ6ebvdYVQ4BM9x7772nxMREJScna9euXWrfvr3i4uJ08uRJu0eDl+Xk5Kh9+/aaPXu23aPAx9atW6eEhARt2bJFq1atUkFBgW699Vbl5OTYPVqVwmn0huvSpYtuuOEGzZo1S5JUXFysJk2aaNy4cXr66adtng6+4nA4tHTpUg0aNMjuUVAJTp06pQYNGmjdunXq2bOn3eNUGeyBGSw/P187d+5Unz59XMtq1KihPn36aPPmzTZOBsCbsrKyJEn16tWzeZKqhYAZ7PTp0yoqKlLDhg1LLG/YsKGOHz9u01QAvKm4uFgTJkxQ9+7d1a5dO7vHqVL87B4AAFC2hIQEffXVV9q4caPdo1Q5BMxg4eHhqlmzpk6cOFFi+YkTJxQZGWnTVAC8ZezYsUpLS9P69evVuHFju8epcjiEaDB/f3916tRJq1evdi0rLi7W6tWr1bVrVxsnA3A5LMvS2LFjtXTpUn3xxRe6+uqr7R6pSmIPzHCJiYmKj49X586d9dvf/lYzZsxQTk6ORowYYfdo8LLs7GwdOHDAdf3w4cPavXu36tWrp6ZNm9o4GbwtISFB77zzjj766CMFBQW53tMOCQlRQECAzdNVHZxGfwWYNWuWXnnlFR0/flwdOnTQzJkz1aVLF7vHgpetXbtWvXr1KrU8Pj5eCxYsqPyB4DMOh+Oiy9966y0NHz68coepwggYAMBIvAcGADASAQMAGImAAQCMRMAAAEYiYAAAIxEwAICRCBgAwEgEDABgJAIGXKbhw4eX+GLJm2++WRMmTKj0OdauXSuHw6HMzEyfPcaFz/VSVMacqB4IGK5Iw4cPl8PhkMPhkL+/v6KjozV16lQVFhb6/LE//PBD/eEPf3DrtpX9P/PmzZtrxowZlfJYgK/xYb64YvXr109vvfWW8vLytGLFCiUkJKhWrVpKSkoqddv8/Hz5+/t75XH51lygcrAHhiuW0+lUZGSkmjVrpjFjxqhPnz76+OOPJf3nUNgLL7ygRo0aqXXr1pKkY8eOaciQIQoNDVW9evU0cOBAHTlyxHWfRUVFSkxMVGhoqOrXr68nn3xSF36c6IWHEPPy8vTUU0+pSZMmcjqdio6O1p///GcdOXLE9eG8YWFhcjgcrg9qLS4uVmpqqq6++moFBASoffv2+tvf/lbicVasWKFWrVopICBAvXr1KjHnpSgqKtJDDz3keszWrVvr1VdfvehtU1JSFBERoeDgYI0ePVr5+fmude7M/t++++47DRgwQGFhYapbt65+85vfaMWKFZf1XFA9sAeGaiMgIEA//vij6/rq1asVHBysVatWSZIKCgoUFxenrl27asOGDfLz89Pzzz+vfv36ae/evfL399e0adO0YMECzZ8/X23atNG0adO0dOlS3XLLLWU+7rBhw7R582bNnDlT7du31+HDh3X69Gk1adJES5Ys0eDBg5Wenq7g4GDXV2Wkpqbq7bff1ty5c9WyZUutX79eQ4cOVUREhGJjY3Xs2DHdeeedSkhI0KhRo7Rjxw49/vjjl/X3U1xcrMaNG+uDDz5Q/fr1tWnTJo0aNUpRUVEaMmRIib+32rVra+3atTpy5IhGjBih+vXr64UXXnBr9gslJCQoPz9f69evV926dbVv3z4FBgZe1nNBNWEBV6D4+Hhr4MCBlmVZVnFxsbVq1SrL6XRakyZNcq1v2LChlZeX5/qZRYsWWa1bt7aKi4tdy/Ly8qyAgABr5cqVlmVZVlRUlPXyyy+71hcUFFiNGzd2PZZlWVZsbKw1fvx4y7IsKz093ZJkrVq16qJzrlmzxpJknTlzxrXs/PnzVp06daxNmzaVuO1DDz1k3XvvvZZlWVZSUpLVtm3bEuufeuqpUvd1oWbNmlnTp08vc/2FEhISrMGDB7uux8fHW/Xq1bNycnJcy+bMmWMFBgZaRUVFbs1+4XOOiYmxpkyZ4vZMwK/YA8MVKy0tTYGBgSooKFBxcbHuu+8+TZkyxbU+JiamxPtee/bs0YEDBxQUFFTifs6fP6+DBw8qKytLGRkZJb5rzc/PT507dy51GPFXu3fvVs2aNS+651GWAwcO6Ny5c+rbt2+J5fn5+erYsaMk6Ztvvin1nW/e+Bbu2bNna/78+Tp69Khyc3OVn5+vDh06lLhN+/btVadOnRKPm52drWPHjik7O7vC2S/02GOPacyYMfrss8/Up08fDR48WNddd91lPxdc+QgYrli9evXSnDlz5O/vr0aNGsnPr+R/7nXr1i1xPTs7W506ddLixYtL3VdERMQlzXAp356bnZ0tSVq+fLmuuuqqEuucTuclzeGOv/71r5o0aZKmTZumrl27KigoSK+88oq2bt3q9n1cyuwjR45UXFycli9frs8++0ypqamaNm2axo0bd+lPBtUCAcMVq27duoqOjnb79tdff73ee+89NWjQQMHBwRe9TVRUlLZu3aqePXtKkgoLC7Vz505df/31F719TEyMiouLtW7dOvXp06fU+l/3AIuKilzL2rZtK6fTqaNHj5a559amTRvXCSm/2rJlS8VPshz/+Mc/1K1bNz366KOuZQcPHix1uz179ig3N9cV5y1btigwMFBNmjRRvXr1Kpz9Ypo0aaLRo0dr9OjRSkpK0rx58wgYKsRZiMD/d//99ys8PFwDBw7Uhg0bdPjwYa1du1aPPfaYvv/+e0nS+PHj9dJLL2nZsmX617/+pUcffbTc3+Fq3ry54uPj9eCDD2rZsmWu+3z//fclSc2aNZPD4VBaWppOnTql7OxsBQUFadKkSZo4caIWLlyogwcPateuXXrttde0cOFCSdLo0aP17bff6oknnlB6erreeecdLViwwK3n+cMPP2j37t0lLmfOnFHLli21Y8cOrVy5Uvv379fkyZO1ffv2Uj+fn5+vhx56SPv27dOKFSuUnJyssWPHqkaNGm7NfqEJEyZo5cqVOnz4sHbt2qU1a9aoTZs2bj0XVHN2vwkH+MJ/n8ThyfqMjAxr2LBhVnh4uOV0Oq0WLVpYDz/8sJWVlWVZ1i8nbYwfP94KDg62QkNDrcTERGvYsGFlnsRhWZaVm5trTZw40YqKirL8/f2t6Ohoa/78+a71U6dOtSIjIy2Hw2HFx8dblvXLiSczZsywWrdubdWqVcuKiIiw4uLirHXr1rl+7pNPPrGio6Mtp9Np9ejRw5o/f75bJ3FIKnVZtGiRdf78eWv48OFWSEiIFRoaao0ZM8Z6+umnrfbt25f6e3vuuees+vXrW4GBgdbDDz9snT9/3nWbima/8CSOsWPHWtdcc43ldDqtiIgI64EHHrBOnz5d5nMAfuWwrDLefQYAoArjECIAwEgEDABgJAIGADASAQMAGImAAQCMRMAAAEYiYAAAIxEwAICRCBgAwEgEDABgJAIGADDS/wIr5Q2U0W4/KwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification Report:\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "#T_4d7a5_row0_col0, #T_4d7a5_row0_col1, #T_4d7a5_row0_col2, #T_4d7a5_row0_col3, #T_4d7a5_row0_col4, #T_4d7a5_row0_col5, #T_4d7a5_row1_col0, #T_4d7a5_row1_col1, #T_4d7a5_row1_col2, #T_4d7a5_row1_col3, #T_4d7a5_row1_col4, #T_4d7a5_row1_col5, #T_4d7a5_row2_col0, #T_4d7a5_row2_col1, #T_4d7a5_row2_col2, #T_4d7a5_row2_col3, #T_4d7a5_row2_col4, #T_4d7a5_row2_col5, #T_4d7a5_row3_col3 {\n",
       "  background-color: #440154;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_4d7a5_row3_col0, #T_4d7a5_row3_col1, #T_4d7a5_row3_col2, #T_4d7a5_row3_col4, #T_4d7a5_row3_col5 {\n",
       "  background-color: #fde725;\n",
       "  color: #000000;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_4d7a5\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_4d7a5_level0_col0\" class=\"col_heading level0 col0\" >0</th>\n",
       "      <th id=\"T_4d7a5_level0_col1\" class=\"col_heading level0 col1\" >1</th>\n",
       "      <th id=\"T_4d7a5_level0_col2\" class=\"col_heading level0 col2\" >2</th>\n",
       "      <th id=\"T_4d7a5_level0_col3\" class=\"col_heading level0 col3\" >accuracy</th>\n",
       "      <th id=\"T_4d7a5_level0_col4\" class=\"col_heading level0 col4\" >macro avg</th>\n",
       "      <th id=\"T_4d7a5_level0_col5\" class=\"col_heading level0 col5\" >weighted avg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_4d7a5_level0_row0\" class=\"row_heading level0 row0\" >precision</th>\n",
       "      <td id=\"T_4d7a5_row0_col0\" class=\"data row0 col0\" >1.000000</td>\n",
       "      <td id=\"T_4d7a5_row0_col1\" class=\"data row0 col1\" >1.000000</td>\n",
       "      <td id=\"T_4d7a5_row0_col2\" class=\"data row0 col2\" >1.000000</td>\n",
       "      <td id=\"T_4d7a5_row0_col3\" class=\"data row0 col3\" >1.000000</td>\n",
       "      <td id=\"T_4d7a5_row0_col4\" class=\"data row0 col4\" >1.000000</td>\n",
       "      <td id=\"T_4d7a5_row0_col5\" class=\"data row0 col5\" >1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4d7a5_level0_row1\" class=\"row_heading level0 row1\" >recall</th>\n",
       "      <td id=\"T_4d7a5_row1_col0\" class=\"data row1 col0\" >1.000000</td>\n",
       "      <td id=\"T_4d7a5_row1_col1\" class=\"data row1 col1\" >1.000000</td>\n",
       "      <td id=\"T_4d7a5_row1_col2\" class=\"data row1 col2\" >1.000000</td>\n",
       "      <td id=\"T_4d7a5_row1_col3\" class=\"data row1 col3\" >1.000000</td>\n",
       "      <td id=\"T_4d7a5_row1_col4\" class=\"data row1 col4\" >1.000000</td>\n",
       "      <td id=\"T_4d7a5_row1_col5\" class=\"data row1 col5\" >1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4d7a5_level0_row2\" class=\"row_heading level0 row2\" >f1-score</th>\n",
       "      <td id=\"T_4d7a5_row2_col0\" class=\"data row2 col0\" >1.000000</td>\n",
       "      <td id=\"T_4d7a5_row2_col1\" class=\"data row2 col1\" >1.000000</td>\n",
       "      <td id=\"T_4d7a5_row2_col2\" class=\"data row2 col2\" >1.000000</td>\n",
       "      <td id=\"T_4d7a5_row2_col3\" class=\"data row2 col3\" >1.000000</td>\n",
       "      <td id=\"T_4d7a5_row2_col4\" class=\"data row2 col4\" >1.000000</td>\n",
       "      <td id=\"T_4d7a5_row2_col5\" class=\"data row2 col5\" >1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4d7a5_level0_row3\" class=\"row_heading level0 row3\" >support</th>\n",
       "      <td id=\"T_4d7a5_row3_col0\" class=\"data row3 col0\" >10.000000</td>\n",
       "      <td id=\"T_4d7a5_row3_col1\" class=\"data row3 col1\" >13.000000</td>\n",
       "      <td id=\"T_4d7a5_row3_col2\" class=\"data row3 col2\" >7.000000</td>\n",
       "      <td id=\"T_4d7a5_row3_col3\" class=\"data row3 col3\" >1.000000</td>\n",
       "      <td id=\"T_4d7a5_row3_col4\" class=\"data row3 col4\" >30.000000</td>\n",
       "      <td id=\"T_4d7a5_row3_col5\" class=\"data row3 col5\" >30.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x2d0ffe8a6d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hackathon_problem(\"XG_Boost.rtf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6b7fa931-5b4e-4a3b-9a57-26b56951f5d6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "91feff94-cfd2-40b6-b609-51f04cf884f3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "95d4d5ab-e33a-44f0-93f1-b9be3b781f3b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}