notebooka6fd166bcc.ipynb

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     "text": [
      "/kaggle/input/who-is-the-real-winner/sample_submission.csv\n",
      "/kaggle/input/who-is-the-real-winner/train.csv\n",
      "/kaggle/input/who-is-the-real-winner/test.csv\n"
     ]
    }
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   "source": [
    "# This Python 3 environment comes with many helpful analytics libraries installed\n",
    "# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n",
    "# For example, here's several helpful packages to load\n",
    "\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "\n",
    "# Input data files are available in the read-only \"../input/\" directory\n",
    "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
    "\n",
    "import os\n",
    "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
    "    for filename in filenames:\n",
    "        print(os.path.join(dirname, filename))\n",
    "\n",
    "# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n",
    "# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"
   ]
  },
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Candidate</th>\n",
       "      <th>Constituency ∇</th>\n",
       "      <th>Party</th>\n",
       "      <th>Criminal Case</th>\n",
       "      <th>Total Assets</th>\n",
       "      <th>Liabilities</th>\n",
       "      <th>state</th>\n",
       "      <th>Education</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>M.K. Mohan</td>\n",
       "      <td>ANNA NAGAR</td>\n",
       "      <td>DMK</td>\n",
       "      <td>4</td>\n",
       "      <td>211 Crore+</td>\n",
       "      <td>2 Crore+</td>\n",
       "      <td>TAMIL NADU</td>\n",
       "      <td>8th Pass</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Khatik Ramesh Prasad</td>\n",
       "      <td>KARERA (SC)</td>\n",
       "      <td>BJP</td>\n",
       "      <td>0</td>\n",
       "      <td>1 Crore+</td>\n",
       "      <td>0</td>\n",
       "      <td>MADHYA PRADESH</td>\n",
       "      <td>12th Pass</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Dr. Mantar Gowda</td>\n",
       "      <td>MADIKERI</td>\n",
       "      <td>INC</td>\n",
       "      <td>0</td>\n",
       "      <td>7 Crore+</td>\n",
       "      <td>22 Lac+</td>\n",
       "      <td>KARNATAKA</td>\n",
       "      <td>Post Graduate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Kundan Kumar</td>\n",
       "      <td>BEGUSARAI</td>\n",
       "      <td>BJP</td>\n",
       "      <td>0</td>\n",
       "      <td>9 Crore+</td>\n",
       "      <td>24 Lac+</td>\n",
       "      <td>BIHAR</td>\n",
       "      <td>Post Graduate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Swapan Majumder</td>\n",
       "      <td>BANGAON DAKSHIN (SC)</td>\n",
       "      <td>BJP</td>\n",
       "      <td>2</td>\n",
       "      <td>2 Crore+</td>\n",
       "      <td>61 Lac+</td>\n",
       "      <td>WEST BENGAL</td>\n",
       "      <td>8th Pass</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ID             Candidate        Constituency ∇ Party  Criminal Case  \\\n",
       "0   0            M.K. Mohan            ANNA NAGAR   DMK              4   \n",
       "1   1  Khatik Ramesh Prasad           KARERA (SC)   BJP              0   \n",
       "2   2      Dr. Mantar Gowda              MADIKERI   INC              0   \n",
       "3   3          Kundan Kumar             BEGUSARAI   BJP              0   \n",
       "4   4       Swapan Majumder  BANGAON DAKSHIN (SC)   BJP              2   \n",
       "\n",
       "  Total Assets Liabilities           state      Education  \n",
       "0   211 Crore+    2 Crore+      TAMIL NADU       8th Pass  \n",
       "1     1 Crore+           0  MADHYA PRADESH      12th Pass  \n",
       "2     7 Crore+     22 Lac+       KARNATAKA  Post Graduate  \n",
       "3     9 Crore+     24 Lac+           BIHAR  Post Graduate  \n",
       "4     2 Crore+     61 Lac+     WEST BENGAL       8th Pass  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data= pd.read_csv('/kaggle/input/who-is-the-real-winner/train.csv')\n",
    "train_data.head()"
   ]
  },
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Candidate</th>\n",
       "      <th>Constituency ∇</th>\n",
       "      <th>Party</th>\n",
       "      <th>Criminal Case</th>\n",
       "      <th>Total Assets</th>\n",
       "      <th>Liabilities</th>\n",
       "      <th>state</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Geeta Bharat Jain</td>\n",
       "      <td>MEERA BHAYANDAR</td>\n",
       "      <td>IND</td>\n",
       "      <td>2</td>\n",
       "      <td>70 Crore+</td>\n",
       "      <td>11 Crore+</td>\n",
       "      <td>MAHARASHTRA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Becharam Manna</td>\n",
       "      <td>SINGUR</td>\n",
       "      <td>AITC</td>\n",
       "      <td>1</td>\n",
       "      <td>2 Crore+</td>\n",
       "      <td>13 Lac+</td>\n",
       "      <td>WEST BENGAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Sunil Vijay Tingre</td>\n",
       "      <td>VADGAON SHERI</td>\n",
       "      <td>NCP</td>\n",
       "      <td>3</td>\n",
       "      <td>49 Crore+</td>\n",
       "      <td>1 Crore+</td>\n",
       "      <td>MAHARASHTRA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Asit Mazumder (Tapan)</td>\n",
       "      <td>CHUNCHURA</td>\n",
       "      <td>AITC</td>\n",
       "      <td>1</td>\n",
       "      <td>2 Crore+</td>\n",
       "      <td>0</td>\n",
       "      <td>WEST BENGAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Hriday Narayan Singh Patel</td>\n",
       "      <td>SAGRI</td>\n",
       "      <td>SP</td>\n",
       "      <td>0</td>\n",
       "      <td>16 Crore+</td>\n",
       "      <td>2 Crore+</td>\n",
       "      <td>UTTAR PRADESH</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ID                   Candidate   Constituency ∇ Party  Criminal Case  \\\n",
       "0   0           Geeta Bharat Jain  MEERA BHAYANDAR   IND              2   \n",
       "1   1              Becharam Manna           SINGUR  AITC              1   \n",
       "2   2          Sunil Vijay Tingre    VADGAON SHERI   NCP              3   \n",
       "3   3       Asit Mazumder (Tapan)        CHUNCHURA  AITC              1   \n",
       "4   4  Hriday Narayan Singh Patel            SAGRI    SP              0   \n",
       "\n",
       "  Total Assets Liabilities          state  \n",
       "0    70 Crore+   11 Crore+    MAHARASHTRA  \n",
       "1     2 Crore+     13 Lac+    WEST BENGAL  \n",
       "2    49 Crore+    1 Crore+    MAHARASHTRA  \n",
       "3     2 Crore+           0    WEST BENGAL  \n",
       "4    16 Crore+    2 Crore+  UTTAR PRADESH  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data= pd.read_csv('/kaggle/input/who-is-the-real-winner/test.csv')\n",
    "test_data.head()"
   ]
  },
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Party</th>\n",
       "      <th>Criminal Case</th>\n",
       "      <th>state</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>DMK</td>\n",
       "      <td>4</td>\n",
       "      <td>TAMIL NADU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BJP</td>\n",
       "      <td>0</td>\n",
       "      <td>MADHYA PRADESH</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>INC</td>\n",
       "      <td>0</td>\n",
       "      <td>KARNATAKA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BJP</td>\n",
       "      <td>0</td>\n",
       "      <td>BIHAR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BJP</td>\n",
       "      <td>2</td>\n",
       "      <td>WEST BENGAL</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Party  Criminal Case           state\n",
       "0   DMK              4      TAMIL NADU\n",
       "1   BJP              0  MADHYA PRADESH\n",
       "2   INC              0       KARNATAKA\n",
       "3   BJP              0           BIHAR\n",
       "4   BJP              2     WEST BENGAL"
      ]
     },
     "execution_count": 4,
     "metadata": {},
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    }
   ],
   "source": [
    "y=train_data.Education\n",
    "\n",
    "parameters=[\"Party\",\"Criminal Case\",\"state\"]\n",
    "\n",
    "X=train_data[parameters]\n",
    "\n",
    "X.head()"
   ]
  },
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   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_19/874911797.py:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  X[column] = label_encoder.fit_transform(X[column])\n",
      "/tmp/ipykernel_19/874911797.py:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  X[column] = label_encoder.fit_transform(X[column])\n",
      "/tmp/ipykernel_19/874911797.py:14: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  X_test[column] = label_encoder1.fit_transform(X_test[column])\n",
      "/tmp/ipykernel_19/874911797.py:14: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  X_test[column] = label_encoder1.fit_transform(X_test[column])\n"
     ]
    },
    {
     "data": {
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       "<div>\n",
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       "\n",
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       "        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>Party</th>\n",
       "      <th>Criminal Case</th>\n",
       "      <th>state</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Party  Criminal Case  state\n",
       "0      9              2     14\n",
       "1      2              1     27\n",
       "2     13              3     14\n",
       "3      2              1     27\n",
       "4     18              0     25"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "label_encoder = LabelEncoder()\n",
    "y = label_encoder.fit_transform(y)\n",
    "for column in X.columns:\n",
    "    if X[column].dtype == 'object':\n",
    "        X[column] = label_encoder.fit_transform(X[column])\n",
    "#X.head()\n",
    "\n",
    "X_test=test_data[parameters]\n",
    "label_encoder1 = LabelEncoder()\n",
    "y_test = label_encoder1.fit_transform(y)\n",
    "for column in X_test.columns:\n",
    "    if X_test[column].dtype == 'object':\n",
    "        X_test[column] = label_encoder1.fit_transform(X_test[column])\n",
    "X_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "cbcff27b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-14T00:24:05.787869Z",
     "iopub.status.busy": "2024-04-14T00:24:05.787505Z",
     "iopub.status.idle": "2024-04-14T00:24:10.537845Z",
     "shell.execute_reply": "2024-04-14T00:24:10.536411Z"
    },
    "papermill": {
     "duration": 4.760754,
     "end_time": "2024-04-14T00:24:10.541727",
     "exception": false,
     "start_time": "2024-04-14T00:24:05.780973",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3 2.5475728155339805\n",
      "Pridictions\n",
      "1374\n"
     ]
    }
   ],
   "source": [
    "\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn.model_selection import train_test_split\n",
    "import math \n",
    "\n",
    "train_X, val_X, train_y, val_y = train_test_split(X, y, random_state=5)\n",
    "\n",
    "def get_mae(max_leaf_nodes, train_X, val_X, train_y, val_y):\n",
    "    model = RandomForestClassifier(max_leaf_nodes=max_leaf_nodes, random_state=5)\n",
    "    model.fit(train_X, train_y)\n",
    "    preds_val = model.predict(val_X)\n",
    "    mae = mean_absolute_error(val_y, preds_val)\n",
    "    return(mae)\n",
    "\n",
    "candidate_max_leaf_nodes = [3,4,5,6,7,8,9,10,11,12,13,14,15,16,17]\n",
    "\n",
    "minimum= 1e9\n",
    "ans=-1\n",
    "for max_leaf_node in candidate_max_leaf_nodes:\n",
    "    my_mae=get_mae(max_leaf_node, train_X, val_X, train_y, val_y)\n",
    "    if(my_mae<minimum):\n",
    "        minimum=my_mae\n",
    "        ans=max_leaf_node\n",
    "\n",
    "\n",
    "print(ans, minimum)\n",
    "\n",
    "model=RandomForestClassifier(random_state=52)\n",
    "model.fit(X,y)\n",
    "\n",
    "print(\"Pridictions\")\n",
    "list=model.predict(X_test)\n",
    "list1=[]\n",
    "for i in list:\n",
    "    list1.append(math.ceil(i))\n",
    "print(len(list1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a63e36c3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-14T00:24:10.557800Z",
     "iopub.status.busy": "2024-04-14T00:24:10.557421Z",
     "iopub.status.idle": "2024-04-14T00:24:10.572374Z",
     "shell.execute_reply": "2024-04-14T00:24:10.571382Z"
    },
    "papermill": {
     "duration": 0.024089,
     "end_time": "2024-04-14T00:24:10.574590",
     "exception": false,
     "start_time": "2024-04-14T00:24:10.550501",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "submission = pd.DataFrame(columns=['ID',\"Education\"])\n",
    "label_encoder = LabelEncoder()\n",
    "y = label_encoder.fit_transform(train_data.Education)\n",
    "submission['Education'] = label_encoder.inverse_transform(list1)\n",
    "submission['ID'] = test_data['ID']\n",
    "submission.to_csv('/kaggle/working/submission.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "29d2c3c8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-14T00:24:10.587606Z",
     "iopub.status.busy": "2024-04-14T00:24:10.586940Z",
     "iopub.status.idle": "2024-04-14T00:24:11.126613Z",
     "shell.execute_reply": "2024-04-14T00:24:11.125500Z"
    },
    "papermill": {
     "duration": 0.549045,
     "end_time": "2024-04-14T00:24:11.129284",
     "exception": false,
     "start_time": "2024-04-14T00:24:10.580239",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "filtered_data = train_data[train_data['Criminal Case'] >= 3]\n",
    "\n",
    "\n",
    "party_distribution = filtered_data['Party'].value_counts(normalize=True) * 100\n",
    "\n",
    "\n",
    "custom_colors = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd',\n",
    "                 '#8c564b', '#e377c2', '#7f7f7f', '#bcbd22', '#17becf']\n",
    "\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "party_distribution.plot(kind='bar', color=custom_colors)\n",
    "plt.title('Percentage Distribution of Parties with Candidates Having Criminal Records >= 3')\n",
    "plt.xlabel('Party')\n",
    "plt.ylabel('Percentage')\n",
    "plt.xticks(rotation=45)\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "770b22a2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-14T00:24:11.144251Z",
     "iopub.status.busy": "2024-04-14T00:24:11.143065Z",
     "iopub.status.idle": "2024-04-14T00:24:11.456701Z",
     "shell.execute_reply": "2024-04-14T00:24:11.455808Z"
    },
    "papermill": {
     "duration": 0.323319,
     "end_time": "2024-04-14T00:24:11.458885",
     "exception": false,
     "start_time": "2024-04-14T00:24:11.135566",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "def convert_assets(asset_str):\n",
    "    if 'Crore' in asset_str:\n",
    "        asset_value = float(asset_str.split()[0])\n",
    "        return asset_value * 100  \n",
    "    elif 'Lac' in asset_str:\n",
    "        return float(asset_str.split()[0])  \n",
    "    else:\n",
    "        return 0  \n",
    "\n",
    "\n",
    "train_data['Total Assets'] = train_data['Total Assets'].apply(convert_assets)\n",
    "\n",
    "\n",
    "wealthy_data = train_data[train_data['Total Assets'] > 10000]  \n",
    "\n",
    "\n",
    "party_distribution = wealthy_data['Party'].value_counts(normalize=True) * 100\n",
    "\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "party_distribution.plot(kind='pie', autopct='%1.1f%%', startangle=140)\n",
    "plt.title('The percentage distribution of parties with the most wealthy candidates(Assets > 100 Crores)')\n",
    "plt.ylabel('')  \n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  }
 ],
 "metadata": {
  "kaggle": {
   "accelerator": "none",
   "dataSources": [
    {
     "databundleVersionId": 8059709,
     "sourceId": 72632,
     "sourceType": "competition"
    }
   ],
   "dockerImageVersionId": 30684,
   "isGpuEnabled": false,
   "isInternetEnabled": true,
   "language": "python",
   "sourceType": "notebook"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
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   "file_extension": ".py",
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   "default_parameters": {},
   "duration": 11.318524,
   "end_time": "2024-04-14T00:24:12.088810",
   "environment_variables": {},
   "exception": null,
   "input_path": "__notebook__.ipynb",
   "output_path": "__notebook__.ipynb",
   "parameters": {},
   "start_time": "2024-04-14T00:24:00.770286",
   "version": "2.5.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}