diff --git a/Stock Prediction Bank Negara/Dataset/BBNI.JK.csv b/Stock Prediction Bank Negara/Dataset/BBNI.JK.csv
new file mode 100644
index 000000000..90ecabb61
--- /dev/null
+++ b/Stock Prediction Bank Negara/Dataset/BBNI.JK.csv	
@@ -0,0 +1,1242 @@
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diff --git a/Stock Prediction Bank Negara/Images/Screenshot from 2024-01-16 03-55-49.png b/Stock Prediction Bank Negara/Images/Screenshot from 2024-01-16 03-55-49.png
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diff --git a/Stock Prediction Bank Negara/Images/closevsopen.png b/Stock Prediction Bank Negara/Images/closevsopen.png
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diff --git a/Stock Prediction Bank Negara/Model/Bank stock prediction.ipynb b/Stock Prediction Bank Negara/Model/Bank stock prediction.ipynb
new file mode 100644
index 000000000..18dbbe510
--- /dev/null
+++ b/Stock Prediction Bank Negara/Model/Bank stock prediction.ipynb	
@@ -0,0 +1,1756 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "id": "8946768f-f53f-4e15-bc13-b81a9f57b80c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn\n",
+    "from sklearn.ensemble import RandomForestRegressor\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.tree import DecisionTreeRegressor\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from sklearn.svm import SVR\n",
+    "from sklearn.linear_model import Lasso\n",
+    "from sklearn.linear_model import Ridge\n",
+    "from xgboost import XGBRegressor\n",
+    "from sklearn.metrics import mean_absolute_error,mean_squared_error, mean_squared_log_error, r2_score"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "e34a1a80-a77a-414f-b33c-17eee865c7fe",
+   "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>Date</th>\n",
+       "      <th>Open</th>\n",
+       "      <th>High</th>\n",
+       "      <th>Low</th>\n",
+       "      <th>Close</th>\n",
+       "      <th>Adj Close</th>\n",
+       "      <th>Volume</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2019-01-01</td>\n",
+       "      <td>4400.0</td>\n",
+       "      <td>4400.0</td>\n",
+       "      <td>4400.0</td>\n",
+       "      <td>4400.0</td>\n",
+       "      <td>3436.901367</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2019-01-02</td>\n",
+       "      <td>4400.0</td>\n",
+       "      <td>4400.0</td>\n",
+       "      <td>4337.5</td>\n",
+       "      <td>4362.5</td>\n",
+       "      <td>3407.609375</td>\n",
+       "      <td>15681200</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2019-01-03</td>\n",
+       "      <td>4337.5</td>\n",
+       "      <td>4387.5</td>\n",
+       "      <td>4325.0</td>\n",
+       "      <td>4362.5</td>\n",
+       "      <td>3407.609375</td>\n",
+       "      <td>21416600</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2019-01-04</td>\n",
+       "      <td>4362.5</td>\n",
+       "      <td>4400.0</td>\n",
+       "      <td>4337.5</td>\n",
+       "      <td>4362.5</td>\n",
+       "      <td>3407.609375</td>\n",
+       "      <td>41078600</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2019-01-07</td>\n",
+       "      <td>4412.5</td>\n",
+       "      <td>4462.5</td>\n",
+       "      <td>4412.5</td>\n",
+       "      <td>4437.5</td>\n",
+       "      <td>3466.192871</td>\n",
+       "      <td>48108200</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         Date    Open    High     Low   Close    Adj Close    Volume\n",
+       "0  2019-01-01  4400.0  4400.0  4400.0  4400.0  3436.901367         0\n",
+       "1  2019-01-02  4400.0  4400.0  4337.5  4362.5  3407.609375  15681200\n",
+       "2  2019-01-03  4337.5  4387.5  4325.0  4362.5  3407.609375  21416600\n",
+       "3  2019-01-04  4362.5  4400.0  4337.5  4362.5  3407.609375  41078600\n",
+       "4  2019-01-07  4412.5  4462.5  4412.5  4437.5  3466.192871  48108200"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = pd.read_csv(\"Downloads/BBNI.JK.csv\")\n",
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "05ae2901-d836-4cee-9619-2123a387bdd1",
+   "metadata": {},
+   "source": [
+    "## Exploratory Data Analysis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "3110f862-aa69-41d3-810f-f2ab73b10fb3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 1241 entries, 0 to 1240\n",
+      "Data columns (total 7 columns):\n",
+      " #   Column     Non-Null Count  Dtype  \n",
+      "---  ------     --------------  -----  \n",
+      " 0   Date       1241 non-null   object \n",
+      " 1   Open       1241 non-null   float64\n",
+      " 2   High       1241 non-null   float64\n",
+      " 3   Low        1241 non-null   float64\n",
+      " 4   Close      1241 non-null   float64\n",
+      " 5   Adj Close  1241 non-null   float64\n",
+      " 6   Volume     1241 non-null   int64  \n",
+      "dtypes: float64(5), int64(1), object(1)\n",
+      "memory usage: 68.0+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "data.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "7fe0d002-12be-44aa-a572-8021dfe1023b",
+   "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>Open</th>\n",
+       "      <th>High</th>\n",
+       "      <th>Low</th>\n",
+       "      <th>Close</th>\n",
+       "      <th>Adj Close</th>\n",
+       "      <th>Volume</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>1241.000000</td>\n",
+       "      <td>1241.000000</td>\n",
+       "      <td>1241.000000</td>\n",
+       "      <td>1241.000000</td>\n",
+       "      <td>1241.000000</td>\n",
+       "      <td>1.241000e+03</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>3766.676068</td>\n",
+       "      <td>3809.437953</td>\n",
+       "      <td>3718.049960</td>\n",
+       "      <td>3762.947220</td>\n",
+       "      <td>3355.122107</td>\n",
+       "      <td>6.706130e+07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>912.747807</td>\n",
+       "      <td>914.221813</td>\n",
+       "      <td>912.537524</td>\n",
+       "      <td>914.206806</td>\n",
+       "      <td>935.536638</td>\n",
+       "      <td>5.222758e+07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>1580.000000</td>\n",
+       "      <td>1705.000000</td>\n",
+       "      <td>1485.000000</td>\n",
+       "      <td>1580.000000</td>\n",
+       "      <td>1375.536499</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>3000.000000</td>\n",
+       "      <td>3050.000000</td>\n",
+       "      <td>2962.500000</td>\n",
+       "      <td>3000.000000</td>\n",
+       "      <td>2611.778320</td>\n",
+       "      <td>3.538360e+07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>3900.000000</td>\n",
+       "      <td>3950.000000</td>\n",
+       "      <td>3862.500000</td>\n",
+       "      <td>3900.000000</td>\n",
+       "      <td>3313.780518</td>\n",
+       "      <td>5.141560e+07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>4512.500000</td>\n",
+       "      <td>4562.500000</td>\n",
+       "      <td>4487.500000</td>\n",
+       "      <td>4512.500000</td>\n",
+       "      <td>4082.027100</td>\n",
+       "      <td>8.171140e+07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>5675.000000</td>\n",
+       "      <td>5750.000000</td>\n",
+       "      <td>5600.000000</td>\n",
+       "      <td>5650.000000</td>\n",
+       "      <td>5650.000000</td>\n",
+       "      <td>4.440854e+08</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              Open         High          Low        Close    Adj Close  \\\n",
+       "count  1241.000000  1241.000000  1241.000000  1241.000000  1241.000000   \n",
+       "mean   3766.676068  3809.437953  3718.049960  3762.947220  3355.122107   \n",
+       "std     912.747807   914.221813   912.537524   914.206806   935.536638   \n",
+       "min    1580.000000  1705.000000  1485.000000  1580.000000  1375.536499   \n",
+       "25%    3000.000000  3050.000000  2962.500000  3000.000000  2611.778320   \n",
+       "50%    3900.000000  3950.000000  3862.500000  3900.000000  3313.780518   \n",
+       "75%    4512.500000  4562.500000  4487.500000  4512.500000  4082.027100   \n",
+       "max    5675.000000  5750.000000  5600.000000  5650.000000  5650.000000   \n",
+       "\n",
+       "             Volume  \n",
+       "count  1.241000e+03  \n",
+       "mean   6.706130e+07  \n",
+       "std    5.222758e+07  \n",
+       "min    0.000000e+00  \n",
+       "25%    3.538360e+07  \n",
+       "50%    5.141560e+07  \n",
+       "75%    8.171140e+07  \n",
+       "max    4.440854e+08  "
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "d1ded554-58da-421f-9e55-389422b18b0f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: ylabel='Frequency'>"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "data.Close.plot.hist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "a7747a46-c975-429e-b630-e060be27c622",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Sort the dataset in date order (ascending)\n",
+    "data.sort_values(by=[\"Date\"], inplace=True, ascending=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "8f3fdfee-3c72-496c-a0dd-a827fc0922ef",
+   "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>Date</th>\n",
+       "      <th>Open</th>\n",
+       "      <th>High</th>\n",
+       "      <th>Low</th>\n",
+       "      <th>Close</th>\n",
+       "      <th>Adj Close</th>\n",
+       "      <th>Volume</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2019-01-01</td>\n",
+       "      <td>4400.0</td>\n",
+       "      <td>4400.0</td>\n",
+       "      <td>4400.0</td>\n",
+       "      <td>4400.0</td>\n",
+       "      <td>3436.901367</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2019-01-02</td>\n",
+       "      <td>4400.0</td>\n",
+       "      <td>4400.0</td>\n",
+       "      <td>4337.5</td>\n",
+       "      <td>4362.5</td>\n",
+       "      <td>3407.609375</td>\n",
+       "      <td>15681200</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2019-01-03</td>\n",
+       "      <td>4337.5</td>\n",
+       "      <td>4387.5</td>\n",
+       "      <td>4325.0</td>\n",
+       "      <td>4362.5</td>\n",
+       "      <td>3407.609375</td>\n",
+       "      <td>21416600</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2019-01-04</td>\n",
+       "      <td>4362.5</td>\n",
+       "      <td>4400.0</td>\n",
+       "      <td>4337.5</td>\n",
+       "      <td>4362.5</td>\n",
+       "      <td>3407.609375</td>\n",
+       "      <td>41078600</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2019-01-07</td>\n",
+       "      <td>4412.5</td>\n",
+       "      <td>4462.5</td>\n",
+       "      <td>4412.5</td>\n",
+       "      <td>4437.5</td>\n",
+       "      <td>3466.192871</td>\n",
+       "      <td>48108200</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         Date    Open    High     Low   Close    Adj Close    Volume\n",
+       "0  2019-01-01  4400.0  4400.0  4400.0  4400.0  3436.901367         0\n",
+       "1  2019-01-02  4400.0  4400.0  4337.5  4362.5  3407.609375  15681200\n",
+       "2  2019-01-03  4337.5  4387.5  4325.0  4362.5  3407.609375  21416600\n",
+       "3  2019-01-04  4362.5  4400.0  4337.5  4362.5  3407.609375  41078600\n",
+       "4  2019-01-07  4412.5  4462.5  4412.5  4437.5  3466.192871  48108200"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "51938768-8c9c-42d8-8d64-c4b83c16d5a9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Date         0\n",
+       "Open         0\n",
+       "High         0\n",
+       "Low          0\n",
+       "Close        0\n",
+       "Adj Close    0\n",
+       "Volume       0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.isna().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "e55d63f3-05ac-4c41-b42b-6cc5e6eb2db7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data[\"Date\"] = pd.to_datetime(data[\"Date\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "9ea484be-4d09-4835-b2a1-e56245dbda47",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 1241 entries, 0 to 1240\n",
+      "Data columns (total 7 columns):\n",
+      " #   Column     Non-Null Count  Dtype         \n",
+      "---  ------     --------------  -----         \n",
+      " 0   Date       1241 non-null   datetime64[ns]\n",
+      " 1   Open       1241 non-null   float64       \n",
+      " 2   High       1241 non-null   float64       \n",
+      " 3   Low        1241 non-null   float64       \n",
+      " 4   Close      1241 non-null   float64       \n",
+      " 5   Adj Close  1241 non-null   float64       \n",
+      " 6   Volume     1241 non-null   int64         \n",
+      "dtypes: datetime64[ns](1), float64(5), int64(1)\n",
+      "memory usage: 68.0 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "data.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "03734109-323a-4e8f-ba3e-a71bd141e3e3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0   2019-01-01\n",
+       "1   2019-01-02\n",
+       "2   2019-01-03\n",
+       "3   2019-01-04\n",
+       "4   2019-01-07\n",
+       "Name: Date, dtype: datetime64[ns]"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data[\"Date\"].head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bc94b27e-a3af-4a63-ae47-1db1f622becf",
+   "metadata": {},
+   "source": [
+    "## Feature Engineering"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "c761feaf-5a40-4dc6-914c-eff49aa7e9d5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data[\"Sale Year\"] = data.Date.dt.year\n",
+    "data[\"Sale Month\"] = data.Date.dt.month"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "600fcb77-82c5-4fb5-bcd1-2867c0d8c0de",
+   "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>Date</th>\n",
+       "      <th>Open</th>\n",
+       "      <th>High</th>\n",
+       "      <th>Low</th>\n",
+       "      <th>Close</th>\n",
+       "      <th>Adj Close</th>\n",
+       "      <th>Volume</th>\n",
+       "      <th>Sale Year</th>\n",
+       "      <th>Sale Month</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2019-01-01</td>\n",
+       "      <td>4400.0</td>\n",
+       "      <td>4400.0</td>\n",
+       "      <td>4400.0</td>\n",
+       "      <td>4400.0</td>\n",
+       "      <td>3436.901367</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2019-01-02</td>\n",
+       "      <td>4400.0</td>\n",
+       "      <td>4400.0</td>\n",
+       "      <td>4337.5</td>\n",
+       "      <td>4362.5</td>\n",
+       "      <td>3407.609375</td>\n",
+       "      <td>15681200</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2019-01-03</td>\n",
+       "      <td>4337.5</td>\n",
+       "      <td>4387.5</td>\n",
+       "      <td>4325.0</td>\n",
+       "      <td>4362.5</td>\n",
+       "      <td>3407.609375</td>\n",
+       "      <td>21416600</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2019-01-04</td>\n",
+       "      <td>4362.5</td>\n",
+       "      <td>4400.0</td>\n",
+       "      <td>4337.5</td>\n",
+       "      <td>4362.5</td>\n",
+       "      <td>3407.609375</td>\n",
+       "      <td>41078600</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2019-01-07</td>\n",
+       "      <td>4412.5</td>\n",
+       "      <td>4462.5</td>\n",
+       "      <td>4412.5</td>\n",
+       "      <td>4437.5</td>\n",
+       "      <td>3466.192871</td>\n",
+       "      <td>48108200</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        Date    Open    High     Low   Close    Adj Close    Volume  \\\n",
+       "0 2019-01-01  4400.0  4400.0  4400.0  4400.0  3436.901367         0   \n",
+       "1 2019-01-02  4400.0  4400.0  4337.5  4362.5  3407.609375  15681200   \n",
+       "2 2019-01-03  4337.5  4387.5  4325.0  4362.5  3407.609375  21416600   \n",
+       "3 2019-01-04  4362.5  4400.0  4337.5  4362.5  3407.609375  41078600   \n",
+       "4 2019-01-07  4412.5  4462.5  4412.5  4437.5  3466.192871  48108200   \n",
+       "\n",
+       "   Sale Year  Sale Month  \n",
+       "0       2019           1  \n",
+       "1       2019           1  \n",
+       "2       2019           1  \n",
+       "3       2019           1  \n",
+       "4       2019           1  "
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "ca279bf8-c09d-4229-bd2f-95a2e6616272",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1241"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "24d0621b-86ad-47e3-84af-547abecccd30",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data[\"Sale Date\"] = data.Date.dt.day"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "b58725dc-bea4-499f-91fd-214965600a85",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
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+       "    }\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>Date</th>\n",
+       "      <th>Open</th>\n",
+       "      <th>High</th>\n",
+       "      <th>Low</th>\n",
+       "      <th>Close</th>\n",
+       "      <th>Adj Close</th>\n",
+       "      <th>Volume</th>\n",
+       "      <th>Sale Year</th>\n",
+       "      <th>Sale Month</th>\n",
+       "      <th>Sale Date</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2019-01-01</td>\n",
+       "      <td>4400.0</td>\n",
+       "      <td>4400.0</td>\n",
+       "      <td>4400.0</td>\n",
+       "      <td>4400.0</td>\n",
+       "      <td>3436.901367</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2019-01-02</td>\n",
+       "      <td>4400.0</td>\n",
+       "      <td>4400.0</td>\n",
+       "      <td>4337.5</td>\n",
+       "      <td>4362.5</td>\n",
+       "      <td>3407.609375</td>\n",
+       "      <td>15681200</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2019-01-03</td>\n",
+       "      <td>4337.5</td>\n",
+       "      <td>4387.5</td>\n",
+       "      <td>4325.0</td>\n",
+       "      <td>4362.5</td>\n",
+       "      <td>3407.609375</td>\n",
+       "      <td>21416600</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2019-01-04</td>\n",
+       "      <td>4362.5</td>\n",
+       "      <td>4400.0</td>\n",
+       "      <td>4337.5</td>\n",
+       "      <td>4362.5</td>\n",
+       "      <td>3407.609375</td>\n",
+       "      <td>41078600</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2019-01-07</td>\n",
+       "      <td>4412.5</td>\n",
+       "      <td>4462.5</td>\n",
+       "      <td>4412.5</td>\n",
+       "      <td>4437.5</td>\n",
+       "      <td>3466.192871</td>\n",
+       "      <td>48108200</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>1</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        Date    Open    High     Low   Close    Adj Close    Volume  \\\n",
+       "0 2019-01-01  4400.0  4400.0  4400.0  4400.0  3436.901367         0   \n",
+       "1 2019-01-02  4400.0  4400.0  4337.5  4362.5  3407.609375  15681200   \n",
+       "2 2019-01-03  4337.5  4387.5  4325.0  4362.5  3407.609375  21416600   \n",
+       "3 2019-01-04  4362.5  4400.0  4337.5  4362.5  3407.609375  41078600   \n",
+       "4 2019-01-07  4412.5  4462.5  4412.5  4437.5  3466.192871  48108200   \n",
+       "\n",
+       "   Sale Year  Sale Month  Sale Date  \n",
+       "0       2019           1          1  \n",
+       "1       2019           1          2  \n",
+       "2       2019           1          3  \n",
+       "3       2019           1          4  \n",
+       "4       2019           1          7  "
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "ca381504-7764-4b7f-9a9f-9628eaedb8ef",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data.drop([\"Date\"], inplace=True, axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "f12aa030-9e1a-44c0-aa29-e26da7bf3311",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Open</th>\n",
+       "      <th>High</th>\n",
+       "      <th>Low</th>\n",
+       "      <th>Close</th>\n",
+       "      <th>Adj Close</th>\n",
+       "      <th>Volume</th>\n",
+       "      <th>Sale Year</th>\n",
+       "      <th>Sale Month</th>\n",
+       "      <th>Sale Date</th>\n",
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+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>4400.0</td>\n",
+       "      <td>4400.0</td>\n",
+       "      <td>4400.0</td>\n",
+       "      <td>4400.0</td>\n",
+       "      <td>3436.901367</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4400.0</td>\n",
+       "      <td>4400.0</td>\n",
+       "      <td>4337.5</td>\n",
+       "      <td>4362.5</td>\n",
+       "      <td>3407.609375</td>\n",
+       "      <td>15681200</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>4337.5</td>\n",
+       "      <td>4387.5</td>\n",
+       "      <td>4325.0</td>\n",
+       "      <td>4362.5</td>\n",
+       "      <td>3407.609375</td>\n",
+       "      <td>21416600</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4362.5</td>\n",
+       "      <td>4400.0</td>\n",
+       "      <td>4337.5</td>\n",
+       "      <td>4362.5</td>\n",
+       "      <td>3407.609375</td>\n",
+       "      <td>41078600</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4412.5</td>\n",
+       "      <td>4462.5</td>\n",
+       "      <td>4412.5</td>\n",
+       "      <td>4437.5</td>\n",
+       "      <td>3466.192871</td>\n",
+       "      <td>48108200</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>1</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Open    High     Low   Close    Adj Close    Volume  Sale Year  \\\n",
+       "0  4400.0  4400.0  4400.0  4400.0  3436.901367         0       2019   \n",
+       "1  4400.0  4400.0  4337.5  4362.5  3407.609375  15681200       2019   \n",
+       "2  4337.5  4387.5  4325.0  4362.5  3407.609375  21416600       2019   \n",
+       "3  4362.5  4400.0  4337.5  4362.5  3407.609375  41078600       2019   \n",
+       "4  4412.5  4462.5  4412.5  4437.5  3466.192871  48108200       2019   \n",
+       "\n",
+       "   Sale Month  Sale Date  \n",
+       "0           1          1  \n",
+       "1           1          2  \n",
+       "2           1          3  \n",
+       "3           1          4  \n",
+       "4           1          7  "
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "d44145bc-5fe1-4f67-b130-7c587cdfa42e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 1241 entries, 0 to 1240\n",
+      "Data columns (total 9 columns):\n",
+      " #   Column      Non-Null Count  Dtype  \n",
+      "---  ------      --------------  -----  \n",
+      " 0   Open        1241 non-null   float64\n",
+      " 1   High        1241 non-null   float64\n",
+      " 2   Low         1241 non-null   float64\n",
+      " 3   Close       1241 non-null   float64\n",
+      " 4   Adj Close   1241 non-null   float64\n",
+      " 5   Volume      1241 non-null   int64  \n",
+      " 6   Sale Year   1241 non-null   int32  \n",
+      " 7   Sale Month  1241 non-null   int32  \n",
+      " 8   Sale Date   1241 non-null   int32  \n",
+      "dtypes: float64(5), int32(3), int64(1)\n",
+      "memory usage: 72.8 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "data.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ac0a0765-7557-467d-a8ab-3c4f14cf8279",
+   "metadata": {},
+   "source": [
+    "## Analysis using matplotlib and seaborn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "id": "74e90d2d-da9d-47ce-bba5-0e68c39ee3a0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(10, 10))\n",
+    "ax.set(title=\"Close and Open\",\n",
+    "       xlabel=\"Close\",\n",
+    "       ylabel=\"Open\")\n",
+    "ax.legend(\"Target\")\n",
+    "ax.scatter(data[\"Close\"], data[\"Open\"], c=data[\"Close\"])\n",
+    "fig.savefig(\"Documents/Git/ML-Crate/Stock Prediction Bank Negara/Images/closevsopen.png\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "id": "84d6b393-7af8-4a0a-b38f-a04bf2bb6fce",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(10, 10))\n",
+    "ax.set(title=\"Close v/s Date\",\n",
+    "       xlabel=\"Close\",\n",
+    "       ylabel=\"Sale Date\")\n",
+    "ax.legend()\n",
+    "ax.bar(data[\"Close\"], data[\"Sale Date\"])\n",
+    "fig.savefig(\"Documents/Git/ML-Crate/Stock Prediction Bank Negara/Images/closevsdate.png\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "id": "f4624b03-f02f-47ab-ae54-3263b39bda35",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 2000x2000 with 9 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "data.plot.hist(figsize=(20, 20), subplots=True);\n",
+    "fig.savefig(\"Documents/Git/ML-Crate/Stock Prediction Bank Negara/Images/stock.png\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "id": "4628f1ee-c8cb-447f-9971-4e6c51d36998",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2000x2000 with 9 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "data.plot.bar(figsize=(20, 20), subplots=True);\n",
+    "fig.savefig(\"Documents/Git/ML-Crate/Stock Prediction Bank Negara/Images/barplotofeach.png\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2cec80fb-910e-4d92-a2a2-6a7f3513c24b",
+   "metadata": {},
+   "source": [
+    "## Preprocessing of the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "109f9ae7-6804-49a9-8ba8-8e4326ad9ae2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X = data.drop(\"Close\", axis=1)\n",
+    "y = data[\"Close\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "20db9278-c571-4403-909c-bdbe7634f155",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1241, 8)"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.shape(X)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "b623f98d-553a-40d7-805a-01dff28c1cd7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1241,)"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.shape(y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "4dcb5dbf-d9d4-4dec-a226-d842f595fd90",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "9556db4b-34a5-48ad-9813-3665b6f0bda6",
+   "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>Open</th>\n",
+       "      <th>High</th>\n",
+       "      <th>Low</th>\n",
+       "      <th>Adj Close</th>\n",
+       "      <th>Volume</th>\n",
+       "      <th>Sale Year</th>\n",
+       "      <th>Sale Month</th>\n",
+       "      <th>Sale Date</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>474</th>\n",
+       "      <td>2850.0</td>\n",
+       "      <td>2900.0</td>\n",
+       "      <td>2825.0</td>\n",
+       "      <td>2470.306885</td>\n",
+       "      <td>106282800</td>\n",
+       "      <td>2020</td>\n",
+       "      <td>11</td>\n",
+       "      <td>20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>915</th>\n",
+       "      <td>4425.0</td>\n",
+       "      <td>4475.0</td>\n",
+       "      <td>4387.5</td>\n",
+       "      <td>4082.027100</td>\n",
+       "      <td>46548200</td>\n",
+       "      <td>2022</td>\n",
+       "      <td>9</td>\n",
+       "      <td>14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>895</th>\n",
+       "      <td>4262.5</td>\n",
+       "      <td>4287.5</td>\n",
+       "      <td>4237.5</td>\n",
+       "      <td>3932.964355</td>\n",
+       "      <td>36326200</td>\n",
+       "      <td>2022</td>\n",
+       "      <td>8</td>\n",
+       "      <td>16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>844</th>\n",
+       "      <td>4450.0</td>\n",
+       "      <td>4487.5</td>\n",
+       "      <td>4350.0</td>\n",
+       "      <td>4082.027100</td>\n",
+       "      <td>70969000</td>\n",
+       "      <td>2022</td>\n",
+       "      <td>6</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1079</th>\n",
+       "      <td>4475.0</td>\n",
+       "      <td>4487.5</td>\n",
+       "      <td>4350.0</td>\n",
+       "      <td>4375.000000</td>\n",
+       "      <td>101617400</td>\n",
+       "      <td>2023</td>\n",
+       "      <td>5</td>\n",
+       "      <td>16</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        Open    High     Low    Adj Close     Volume  Sale Year  Sale Month  \\\n",
+       "474   2850.0  2900.0  2825.0  2470.306885  106282800       2020          11   \n",
+       "915   4425.0  4475.0  4387.5  4082.027100   46548200       2022           9   \n",
+       "895   4262.5  4287.5  4237.5  3932.964355   36326200       2022           8   \n",
+       "844   4450.0  4487.5  4350.0  4082.027100   70969000       2022           6   \n",
+       "1079  4475.0  4487.5  4350.0  4375.000000  101617400       2023           5   \n",
+       "\n",
+       "      Sale Date  \n",
+       "474          20  \n",
+       "915          14  \n",
+       "895          16  \n",
+       "844           6  \n",
+       "1079         16  "
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_train.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "6c34fd07-27c3-4e9a-8fc9-e18801a96798",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
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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>Open</th>\n",
+       "      <th>High</th>\n",
+       "      <th>Low</th>\n",
+       "      <th>Adj Close</th>\n",
+       "      <th>Volume</th>\n",
+       "      <th>Sale Year</th>\n",
+       "      <th>Sale Month</th>\n",
+       "      <th>Sale Date</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>824</th>\n",
+       "      <td>4750.0</td>\n",
+       "      <td>4850.0</td>\n",
+       "      <td>4737.5</td>\n",
+       "      <td>4403.085449</td>\n",
+       "      <td>79762000</td>\n",
+       "      <td>2022</td>\n",
+       "      <td>4</td>\n",
+       "      <td>26</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>703</th>\n",
+       "      <td>3512.5</td>\n",
+       "      <td>3512.5</td>\n",
+       "      <td>3412.5</td>\n",
+       "      <td>3027.943848</td>\n",
+       "      <td>69669000</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>11</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>463</th>\n",
+       "      <td>2360.0</td>\n",
+       "      <td>2425.0</td>\n",
+       "      <td>2340.0</td>\n",
+       "      <td>2102.481445</td>\n",
+       "      <td>180563400</td>\n",
+       "      <td>2020</td>\n",
+       "      <td>11</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>649</th>\n",
+       "      <td>2562.5</td>\n",
+       "      <td>2575.0</td>\n",
+       "      <td>2525.0</td>\n",
+       "      <td>2254.381592</td>\n",
+       "      <td>17836000</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>8</td>\n",
+       "      <td>13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1156</th>\n",
+       "      <td>4725.0</td>\n",
+       "      <td>4725.0</td>\n",
+       "      <td>4650.0</td>\n",
+       "      <td>4650.000000</td>\n",
+       "      <td>53087200</td>\n",
+       "      <td>2023</td>\n",
+       "      <td>9</td>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        Open    High     Low    Adj Close     Volume  Sale Year  Sale Month  \\\n",
+       "824   4750.0  4850.0  4737.5  4403.085449   79762000       2022           4   \n",
+       "703   3512.5  3512.5  3412.5  3027.943848   69669000       2021          11   \n",
+       "463   2360.0  2425.0  2340.0  2102.481445  180563400       2020          11   \n",
+       "649   2562.5  2575.0  2525.0  2254.381592   17836000       2021           8   \n",
+       "1156  4725.0  4725.0  4650.0  4650.000000   53087200       2023           9   \n",
+       "\n",
+       "      Sale Date  \n",
+       "824          26  \n",
+       "703           1  \n",
+       "463           5  \n",
+       "649          13  \n",
+       "1156         12  "
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_test.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "id": "e6e10933-9c97-4120-9898-cd357b5d0cce",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "474     2837.5\n",
+       "915     4450.0\n",
+       "895     4287.5\n",
+       "844     4450.0\n",
+       "1079    4375.0\n",
+       "Name: Close, dtype: float64"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_train.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "id": "0c96aa8d-5c91-4640-9795-6e265ba3f0c7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scaler = StandardScaler()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "id": "0ae75ad4-dc72-4735-a3fd-7041b0adc738",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scaled_X = scaler.fit_transform(X_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "id": "a032d3f4-a26b-4dbe-adaf-e08c6c26c6f1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([-1.01187941, -1.00161764, -0.98600653, -0.95338817,  0.76651824,\n",
+       "       -0.71588039,  1.30735669,  0.48508487])"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "scaled_X[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 108,
+   "id": "8fb1fc0d-9009-4592-b99a-b4be74c0c67a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def score(y_true, y_pred):\n",
+    "    value = {\"Mean Absolute Error \":mean_absolute_error(y_true, y_pred),\n",
+    "             \"Mean Squared Error \":mean_squared_error(y_true, y_pred),\n",
+    "             \"R2 Score\":r2_score(y_true, y_pred)}\n",
+    "    return value"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e776c824-46ff-49bd-9f3b-aac79bd333d7",
+   "metadata": {},
+   "source": [
+    "## Model and prediction"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 99,
+   "id": "02618ede-e245-4d54-a416-65e01b3c1c2c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:458: UserWarning: X has feature names, but RandomForestRegressor was fitted without feature names\n",
+      "  warnings.warn(\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Random Forest\n",
+    "clf_forest = RandomForestRegressor()\n",
+    "clf_forest.fit(scaled_X, y_train)\n",
+    "clf_pred_forest = clf_forest.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 100,
+   "id": "bee3ebba-20fa-40fd-a98b-758b8b7177be",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'Mean Absolute Error ': 1812.632530120482,\n",
+       " 'Mean Squared Error ': 4156135.4708835343,\n",
+       " 'Mean Squared Log Error': 0.2614044917078193,\n",
+       " 'R2 Score': -3.7541602067165227}"
+      ]
+     },
+     "execution_count": 100,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "score(y_test, clf_pred_forest)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "id": "9d0517d0-d71d-4b62-8725-547deb86e94a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:458: UserWarning: X has feature names, but DecisionTreeRegressor was fitted without feature names\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'Mean Absolute Error ': 1838.0220883534137,\n",
+       " 'Mean Squared Error ': 4251059.262048192,\n",
+       " 'Mean Squared Log Error': 0.2654177334185552,\n",
+       " 'R2 Score': -3.86274254571563}"
+      ]
+     },
+     "execution_count": 104,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Decision Tree Regression\n",
+    "clf_log = DecisionTreeRegressor()\n",
+    "clf_log.fit(scaled_X, y_train)\n",
+    "clf_pred_log = clf_log.predict(X_test)\n",
+    "score(y_test, clf_pred_log)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "id": "d8cb6fa6-7712-4327-b09b-5bd50c7a4b5f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:458: UserWarning: X has feature names, but SVR was fitted without feature names\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'Mean Absolute Error ': 785.5900404808178,\n",
+       " 'Mean Squared Error ': 888059.9662582307,\n",
+       " 'Mean Squared Log Error': 0.08199677250063905,\n",
+       " 'R2 Score': -0.015842573549549543}"
+      ]
+     },
+     "execution_count": 105,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# SVR\n",
+    "clf_svm = SVR()\n",
+    "clf_svm.fit(scaled_X, y_train)\n",
+    "clf_svm_pred = clf_svm.predict(X_test)\n",
+    "score(y_test, clf_svm_pred)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 109,
+   "id": "8fb97f3b-6d42-44fa-acf5-f7946bb343bc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.305e+05, tolerance: 8.187e+04\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:458: UserWarning: X has feature names, but Lasso was fitted without feature names\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'Mean Absolute Error ': 158031999.27773747,\n",
+       " 'Mean Squared Error ': 4.133026897755043e+16,\n",
+       " 'R2 Score': -47277265497.804726}"
+      ]
+     },
+     "execution_count": 109,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Lasso\n",
+    "clf_lasso = Lasso()\n",
+    "clf_lasso.fit(scaled_X, y_train)\n",
+    "clf_lasso_pred = clf_lasso.predict(X_test)\n",
+    "score(y_test, clf_lasso_pred)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1ebc2550-da52-43da-b230-2b631e1137d5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# SVR\n",
+    "clf_ridge = SVR()\n",
+    "clf_ridge.fit(scaled_X, y_train)\n",
+    "clf__pred = clf_ridge.predict(X_test)\n",
+    "score(y_test, clf_ridge_pred)"
+   ]
+  }
+ ],
+ "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": "3.10.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Stock Prediction Bank Negara/Model/README.md b/Stock Prediction Bank Negara/Model/README.md
new file mode 100644
index 000000000..813468740
--- /dev/null
+++ b/Stock Prediction Bank Negara/Model/README.md	
@@ -0,0 +1,75 @@
+
+  
+
+**BANK Negara Stock Prediction**
+
+  
+
+**GOAL**
+
+  
+To predict a the closing price of the bank stock prices from the dataset.
+  
+
+**DATASET**
+
+  
+
+https://www.kaggle.com/datasets/caesarmario/bank-negara-indonesia-stock-historical-price
+
+  
+
+**DESCRIPTION**
+
+  
+
+The main aim of the project is to make a model that helps to predict the closing price of the bank.
+  
+
+**WORK DONE**
+
+* Analyzed the data and found insights such as correlation, missing values etc.
+* Selected the columns that have high correlation than other columns to be used as features. (Refer : `eda-banknote-dataset`)
+* Next trained model with algorithms with default parameters:
+	* Logistic Regression
+	* Linear SVR
+	* Lasso
+	* Ridge
+	* Decision Tree
+	* Random Forest
+	* XGBoost
+* In this Linear SVR and performed the best with 90% accuracy.
+  
+
+**MODELS USED**
+
+1. Logistic Regression : Logistic regression is easier to implement, interpret, and very efficient to train. It is **very fast at classifying unknown records**.
+2. Linear SVM : SVM performs well on classification problems when size of dataset is not too large.
+3. Random Forest : It **provides higher accuracy through cross validation**. Random forest classifier will handle the missing values and maintain the accuracy of a large proportion of data. If there are more trees, it won't allow over-fitting trees in the model.
+4. XGBoost : XGBoost is **a library for developing fast and high performance gradient boosting tree models**. XGBoost achieves the best performance on a range of difficult machine learning tasks.
+5. LightGBM : Light GBM is prefixed as Light because of its high speed. Light GBM can handle the large size of data and takes lower memory to run. it is so popular is because **it focuses on accuracy of results**.
+
+**LIBRARIES NEEDED**
+
+* Numpy
+* Pandas
+* Matplotlib
+* scikit-learn
+* xgboost
+* seaborn
+  
+  
+
+**CONCLUSION**
+
+  
+
+We investigated the data, checking for data unbalancing, visualizing the features, and understanding the relationship between different features. We then investigated two predictive models. The data was split into two parts, a train set, a test set. For the first five base models, we only used the train and test set.
+
+We started with SVR, Decision Tree, Lasso, Ridge, Random Forrest Regressor and XGBoost Regressor for which we obtained an highest accuracy of 90%, when predicting the target for the test set.
+
+  
+
+**CONTRIBUTION BY**
+
+*Pawas Pandey*
diff --git a/Stock Prediction Bank Negara/requirements.txt b/Stock Prediction Bank Negara/requirements.txt
new file mode 100644
index 000000000..be4765543
--- /dev/null
+++ b/Stock Prediction Bank Negara/requirements.txt	
@@ -0,0 +1,5 @@
+pandas
+matplotlib
+numpy
+sklearn
+seaborn
\ No newline at end of file