diff --git "a/crop-recommendation/notebooks/crop-recommendation-notebook.ipynb" "b/crop-recommendation/notebooks/crop-recommendation-notebook.ipynb"
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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# importing the dependancies \n",
+    "import pandas as pd \n",
+    "import numpy as np \n",
+    "import matplotlib.pyplot as plt \n",
+    "import seaborn as sns\n",
+    "\n",
+    "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier, AdaBoostClassifier\n",
+    "from sklearn.tree import DecisionTreeClassifier\n",
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "from xgboost import XGBClassifier\n",
+    "from catboost import CatBoostClassifier\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Dataset Summary\n",
+    "\n",
+    "This dataset was build by augmenting datasets of rainfall, climate and fertilizer data available for India.\n",
+    "\n",
+    "- `N` - ratio of Nitrogen content in soil\n",
+    "- `P` - ratio of Phosphorous content in soil\n",
+    "- `K` - ratio of Potassium content in soil\n",
+    "- `temperature` - temperature in degree Celsius\n",
+    "- `humidity` - relative humidity in %\n",
+    "- `ph` - ph value of the soil\n",
+    "- `rainfall` - rainfall in mm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>N</th>\n",
+       "      <th>P</th>\n",
+       "      <th>K</th>\n",
+       "      <th>temperature</th>\n",
+       "      <th>humidity</th>\n",
+       "      <th>ph</th>\n",
+       "      <th>rainfall</th>\n",
+       "      <th>label</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>90</td>\n",
+       "      <td>42</td>\n",
+       "      <td>43</td>\n",
+       "      <td>20.879744</td>\n",
+       "      <td>82.002744</td>\n",
+       "      <td>6.502985</td>\n",
+       "      <td>202.935536</td>\n",
+       "      <td>rice</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>85</td>\n",
+       "      <td>58</td>\n",
+       "      <td>41</td>\n",
+       "      <td>21.770462</td>\n",
+       "      <td>80.319644</td>\n",
+       "      <td>7.038096</td>\n",
+       "      <td>226.655537</td>\n",
+       "      <td>rice</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>60</td>\n",
+       "      <td>55</td>\n",
+       "      <td>44</td>\n",
+       "      <td>23.004459</td>\n",
+       "      <td>82.320763</td>\n",
+       "      <td>7.840207</td>\n",
+       "      <td>263.964248</td>\n",
+       "      <td>rice</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>74</td>\n",
+       "      <td>35</td>\n",
+       "      <td>40</td>\n",
+       "      <td>26.491096</td>\n",
+       "      <td>80.158363</td>\n",
+       "      <td>6.980401</td>\n",
+       "      <td>242.864034</td>\n",
+       "      <td>rice</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>78</td>\n",
+       "      <td>42</td>\n",
+       "      <td>42</td>\n",
+       "      <td>20.130175</td>\n",
+       "      <td>81.604873</td>\n",
+       "      <td>7.628473</td>\n",
+       "      <td>262.717340</td>\n",
+       "      <td>rice</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    N   P   K  temperature   humidity        ph    rainfall label\n",
+       "0  90  42  43    20.879744  82.002744  6.502985  202.935536  rice\n",
+       "1  85  58  41    21.770462  80.319644  7.038096  226.655537  rice\n",
+       "2  60  55  44    23.004459  82.320763  7.840207  263.964248  rice\n",
+       "3  74  35  40    26.491096  80.158363  6.980401  242.864034  rice\n",
+       "4  78  42  42    20.130175  81.604873  7.628473  262.717340  rice"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "FILE_PATH = \"/config/workspace/crop-recommendation-dataset/Crop_recommendation.csv\"\n",
+    "\n",
+    "df = pd.read_csv(FILE_PATH)\n",
+    "\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Shape of the dataset: (2200, 8)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f\"Shape of the dataset: {df.shape}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "N              0\n",
+       "P              0\n",
+       "K              0\n",
+       "temperature    0\n",
+       "humidity       0\n",
+       "ph             0\n",
+       "rainfall       0\n",
+       "label          0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# checking for the null values in the dataset \n",
+    "df.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>N</th>\n",
+       "      <th>P</th>\n",
+       "      <th>K</th>\n",
+       "      <th>temperature</th>\n",
+       "      <th>humidity</th>\n",
+       "      <th>ph</th>\n",
+       "      <th>rainfall</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>2200.000000</td>\n",
+       "      <td>2200.000000</td>\n",
+       "      <td>2200.000000</td>\n",
+       "      <td>2200.000000</td>\n",
+       "      <td>2200.000000</td>\n",
+       "      <td>2200.000000</td>\n",
+       "      <td>2200.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>50.551818</td>\n",
+       "      <td>53.362727</td>\n",
+       "      <td>48.149091</td>\n",
+       "      <td>25.616244</td>\n",
+       "      <td>71.481779</td>\n",
+       "      <td>6.469480</td>\n",
+       "      <td>103.463655</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>36.917334</td>\n",
+       "      <td>32.985883</td>\n",
+       "      <td>50.647931</td>\n",
+       "      <td>5.063749</td>\n",
+       "      <td>22.263812</td>\n",
+       "      <td>0.773938</td>\n",
+       "      <td>54.958389</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>8.825675</td>\n",
+       "      <td>14.258040</td>\n",
+       "      <td>3.504752</td>\n",
+       "      <td>20.211267</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>21.000000</td>\n",
+       "      <td>28.000000</td>\n",
+       "      <td>20.000000</td>\n",
+       "      <td>22.769375</td>\n",
+       "      <td>60.261953</td>\n",
+       "      <td>5.971693</td>\n",
+       "      <td>64.551686</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>37.000000</td>\n",
+       "      <td>51.000000</td>\n",
+       "      <td>32.000000</td>\n",
+       "      <td>25.598693</td>\n",
+       "      <td>80.473146</td>\n",
+       "      <td>6.425045</td>\n",
+       "      <td>94.867624</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>84.250000</td>\n",
+       "      <td>68.000000</td>\n",
+       "      <td>49.000000</td>\n",
+       "      <td>28.561654</td>\n",
+       "      <td>89.948771</td>\n",
+       "      <td>6.923643</td>\n",
+       "      <td>124.267508</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>140.000000</td>\n",
+       "      <td>145.000000</td>\n",
+       "      <td>205.000000</td>\n",
+       "      <td>43.675493</td>\n",
+       "      <td>99.981876</td>\n",
+       "      <td>9.935091</td>\n",
+       "      <td>298.560117</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                 N            P            K  temperature     humidity  \\\n",
+       "count  2200.000000  2200.000000  2200.000000  2200.000000  2200.000000   \n",
+       "mean     50.551818    53.362727    48.149091    25.616244    71.481779   \n",
+       "std      36.917334    32.985883    50.647931     5.063749    22.263812   \n",
+       "min       0.000000     5.000000     5.000000     8.825675    14.258040   \n",
+       "25%      21.000000    28.000000    20.000000    22.769375    60.261953   \n",
+       "50%      37.000000    51.000000    32.000000    25.598693    80.473146   \n",
+       "75%      84.250000    68.000000    49.000000    28.561654    89.948771   \n",
+       "max     140.000000   145.000000   205.000000    43.675493    99.981876   \n",
+       "\n",
+       "                ph     rainfall  \n",
+       "count  2200.000000  2200.000000  \n",
+       "mean      6.469480   103.463655  \n",
+       "std       0.773938    54.958389  \n",
+       "min       3.504752    20.211267  \n",
+       "25%       5.971693    64.551686  \n",
+       "50%       6.425045    94.867624  \n",
+       "75%       6.923643   124.267508  \n",
+       "max       9.935091   298.560117  "
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1500x700 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.set(style = \"whitegrid\")\n",
+    "plt.figure(figsize = (15,7))\n",
+    "\n",
+    "ax = sns.countplot(data = df, x= 'label')\n",
+    "\n",
+    "ax.set(xlabel='crop name')\n",
+    "plt.xticks(rotation = 45)\n",
+    "\n",
+    "ax.set(ylabel = 'data points')\n",
+    "\n",
+    "plt.title(\"Dataset Distribution\", fontsize = 20)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 700x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "corr_matrix = df._get_numeric_data().corr()\n",
+    "\n",
+    "plt.figure(figsize=(7, 5))\n",
+    "\n",
+    "sns.heatmap(corr_matrix, annot=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Shape of the train data: (1760, 7)\n",
+      "Shape of the test data: (440, 7)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "X = df.drop(columns=['label'])\n",
+    "y = df['label']\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
+    "\n",
+    "print(f\"Shape of the train data: {X_train.shape}\")\n",
+    "print(f\"Shape of the test data: {X_test.shape}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "\n",
+    "le = LabelEncoder()\n",
+    "\n",
+    "y_train_transformed = le.fit_transform(y_train)\n",
+    "y_test_transformed = le.transform(y_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score, roc_auc_score\n",
+    "\n",
+    "def evaluate_clf(true, predicted):\n",
+    "    '''\n",
+    "    This function takes in true values and predicted values\n",
+    "    Returns: Accuracy, F1-Score, Precision, Recall, Roc-auc Score\n",
+    "    '''\n",
+    "    acc = accuracy_score(true, predicted)\n",
+    "    f1 = f1_score(true, predicted, average='weighted')\n",
+    "    precision = precision_score(true, predicted, average='weighted')\n",
+    "    recall = recall_score(true, predicted, average='weighted')\n",
+    "    \n",
+    "    return acc, f1, precision, recall"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# create a function which can evaluate models and returns a report \n",
+    "def evaluate_model(X_train, X_test, y_train, y_test, models):\n",
+    "    '''\n",
+    "    This function takes X_train, X_test, y_train, y_test  and models dictionary as input\n",
+    "    Iterate through the given model directory and evaluate metrics\n",
+    "\n",
+    "    Returns:\n",
+    "    DataFrame which contains report of all models metrics \n",
+    "    '''\n",
+    "\n",
+    "    model_list = []\n",
+    "    metric_list = []\n",
+    "\n",
+    "    for i in range(len(list(models))):\n",
+    "        model = list(models.values())[i]\n",
+    "        model.fit(X_train, y_train)\n",
+    "\n",
+    "        # Make predictions\n",
+    "        y_train_pred = model.predict(X_train)\n",
+    "        y_test_pred = model.predict(X_test)\n",
+    "\n",
+    "        # Training set performances\n",
+    "        model_train_accuracy, model_train_f1, model_train_precision, \\\n",
+    "            model_train_recall = evaluate_clf(y_train, y_train_pred)\n",
+    "\n",
+    "        # Test set peformances \n",
+    "        model_test_accuracy, model_test_f1, model_test_precision, \\\n",
+    "            model_test_recall = evaluate_clf(y_test, y_test_pred)\n",
+    "\n",
+    "        print(list(models.keys())[i])\n",
+    "        model_list.append(list(models.keys())[i])\n",
+    "\n",
+    "        result_dict ={'model_name':list(models.keys())[i], \n",
+    "                    \"train_accuracy\": model_train_accuracy, \"test_accuracy\": model_test_accuracy,\n",
+    "                    \"train_precision\": model_train_precision, \"test_precision\": model_test_precision,\n",
+    "                    'train_recall': model_train_recall, \"test_recall\":model_test_recall,\n",
+    "                    \"train_f1_score\": model_train_f1, \"test_f1_score\": model_test_f1}\n",
+    "\n",
+    "        metric_list.append(result_dict)\n",
+    "\n",
+    "    \n",
+    "    return metric_list\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Model Dictionary\n",
+    "models = {\n",
+    "    \"Random Forest\": RandomForestClassifier(),\n",
+    "    \"Decision Tree\": DecisionTreeClassifier(),\n",
+    "    \"Gradient Boosting\": GradientBoostingClassifier(),\n",
+    "     \"K-Neighbors Classifier\": KNeighborsClassifier(),\n",
+    "    \"XGBClassifier\": XGBClassifier(), \n",
+    "     \"CatBoosting Classifier\": CatBoostClassifier(verbose=False),\n",
+    "    \"AdaBoost Classifier\": AdaBoostClassifier()\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "resultant_metrics = evaluate_model(X_train, X_test, y_train_transformed, y_test_transformed, models)\n",
+    "\n",
+    "resultant_metrics_df = pd.DataFrame(data=resultant_metrics)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>model_name</th>\n",
+       "      <th>train_accuracy</th>\n",
+       "      <th>test_accuracy</th>\n",
+       "      <th>train_precision</th>\n",
+       "      <th>test_precision</th>\n",
+       "      <th>train_recall</th>\n",
+       "      <th>test_recall</th>\n",
+       "      <th>train_f1_score</th>\n",
+       "      <th>test_f1_score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Random Forest</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.993182</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.993735</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.993182</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.993175</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>XGBClassifier</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.990909</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.991447</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.990909</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.990893</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>CatBoosting Classifier</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.988636</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.989808</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.988636</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.988698</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Gradient Boosting</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.981818</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.984271</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.981818</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.981851</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Decision Tree</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.981818</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.982331</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.981818</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.981809</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>K-Neighbors Classifier</td>\n",
+       "      <td>0.989773</td>\n",
+       "      <td>0.970455</td>\n",
+       "      <td>0.990106</td>\n",
+       "      <td>0.973976</td>\n",
+       "      <td>0.989773</td>\n",
+       "      <td>0.970455</td>\n",
+       "      <td>0.989798</td>\n",
+       "      <td>0.970311</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>AdaBoost Classifier</td>\n",
+       "      <td>0.192045</td>\n",
+       "      <td>0.140909</td>\n",
+       "      <td>0.099862</td>\n",
+       "      <td>0.071247</td>\n",
+       "      <td>0.192045</td>\n",
+       "      <td>0.140909</td>\n",
+       "      <td>0.118204</td>\n",
+       "      <td>0.085220</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               model_name  train_accuracy  test_accuracy  train_precision  \\\n",
+       "0           Random Forest        1.000000       0.993182         1.000000   \n",
+       "4           XGBClassifier        1.000000       0.990909         1.000000   \n",
+       "5  CatBoosting Classifier        1.000000       0.988636         1.000000   \n",
+       "2       Gradient Boosting        1.000000       0.981818         1.000000   \n",
+       "1           Decision Tree        1.000000       0.981818         1.000000   \n",
+       "3  K-Neighbors Classifier        0.989773       0.970455         0.990106   \n",
+       "6     AdaBoost Classifier        0.192045       0.140909         0.099862   \n",
+       "\n",
+       "   test_precision  train_recall  test_recall  train_f1_score  test_f1_score  \n",
+       "0        0.993735      1.000000     0.993182        1.000000       0.993175  \n",
+       "4        0.991447      1.000000     0.990909        1.000000       0.990893  \n",
+       "5        0.989808      1.000000     0.988636        1.000000       0.988698  \n",
+       "2        0.984271      1.000000     0.981818        1.000000       0.981851  \n",
+       "1        0.982331      1.000000     0.981818        1.000000       0.981809  \n",
+       "3        0.973976      0.989773     0.970455        0.989798       0.970311  \n",
+       "6        0.071247      0.192045     0.140909        0.118204       0.085220  "
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "resultant_metrics_df = resultant_metrics_df.sort_values(by='test_f1_score', ascending=False)\n",
+    "resultant_metrics_df"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.8.10 64-bit",
+   "language": "python",
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