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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "b3fea80c-f5ab-4a63-b29e-e5043fd7c96e",
+   "metadata": {},
+   "source": [
+    "## Dataset exploration"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ea1a24a8-1942-494a-a795-96d3d1f07dae",
+   "metadata": {},
+   "source": [
+    "### Setup and Data Loading"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "61b65190-569d-418e-a75c-c91db5106f25",
+   "metadata": {},
+   "source": [
+    "Import necessary libraries:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "04e223dc-b69e-4ebf-ad58-a79e630cb75f",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ebf95787-c0dc-4668-b8ea-0f8479a7b978",
+   "metadata": {},
+   "source": [
+    " Load your data into a Pandas DataFrame:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "740240e1-ad27-47e2-b395-fb08dcadbf1d",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "train_data_orig = pd.read_csv('train_data.csv')\n",
+    "test_data_orig = pd.read_csv('test_data.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "8ce84259-79ea-429d-9e42-91ffa23edcb6",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "#train_data_orig"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "237e8e44-62e8-4d0f-9da4-bb6fe7b8c8c6",
+   "metadata": {},
+   "source": [
+    "### Data Merging/Concatenation (if necessary)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "5a4300f9-60a4-414a-9a4c-684bbc43029e",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# If concatenating vertically (append data in rows)\n",
+    "conc_data = pd.concat([train_data_orig, test_data_orig], ignore_index=True)\n",
+    "\n",
+    "# If merging based on a common column (e.g., 'output')\n",
+    "# conc_data = pd.merge(train_data_orig, test_data_orig, on='output', how='inner')\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "331c8f02-01a7-49c2-b976-017e7627dbf4",
+   "metadata": {},
+   "source": [
+    "### Normalization\n",
+    "Data normalization is a technique used to change the values of numeric columns in the dataset to a common scale."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "3ebbb0aa-5149-4e25-adb4-67ab883dbf19",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# Mannual Normalisation:\n",
+    "#numerical_cols = ['output', 'input1', 'input2', 'input3', ..., 'input21']\n",
+    "\n",
+    "# Programatically\n",
+    "numerical_cols = train_data_orig.select_dtypes(include=np.number).columns.tolist()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "afbe7ca8-848f-4916-8466-de9d06717a68",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    " Handle any missing values in these columns:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "1c4a0bf6-3e68-474c-b24c-444478603922",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# Fill NAs with the mean value of each column\n",
+    "train_data_orig[numerical_cols] = test_data_orig[numerical_cols].apply(lambda x: x.fillna(x.mean()), axis=0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a1da0a70-a48c-4b1b-b150-b8477815eed9",
+   "metadata": {},
+   "source": [
+    "**Apply normalization to the selected columns:**\n",
+    "\n",
+    "Choose a normalization method. The two most common methods are:\n",
+    "\n",
+    " ***1. Min-Max Scaling***\n",
+    "\n",
+    "This method rescales the features to a fixed range, usually [0,1].\n",
+    "\\begin{equation}\n",
+    "X_{\\text{norm}} = \\frac{X - X_{\\text{min}}}{X_{\\text{max}} - X_{\\text{min}}}\n",
+    "\\end{equation}\n",
+    "\n",
+    "Where:\n",
+    "- \\( X \\) is the original value.\n",
+    "- \\( X_{\\text{min}} \\) is the minimum value in the column.\n",
+    "- \\( X_{\\text{max}} \\) is the maximum value in the column.\n",
+    "\n",
+    " ***2. Z-score Normalization (Standardization)***\n",
+    "\n",
+    "This method uses the mean and standard deviation of the feature.\n",
+    "\n",
+    "\\begin{equation}\n",
+    "X_{\\text{std}} = \\frac{X - \\mu}{\\sigma}\n",
+    "\\end{equation}\n",
+    "\n",
+    "Where:\n",
+    "- \\( X \\) is the original value.\n",
+    "- \\( \\mu \\) is the mean of the column.\n",
+    "- \\( \\sigma \\) is the standard deviation of the column."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "f5c85bb1-c5ba-4dcd-bb63-2b7331e1a575",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# Using Min-Max normalization\n",
+    "test_data_orig[numerical_cols] = test_data_orig[numerical_cols].apply(\n",
+    "    lambda x: (x - x.min()) / (x.max() - x.min()), axis=0\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1593accd-45aa-4467-afe5-17349a9217f7",
+   "metadata": {},
+   "source": [
+    "Saving normalized data to a CSV file"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "5c916c35-a519-4d82-bb0c-2becd7b6b72c",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "#train_data.to_csv('path_to_save/normalized_data.csv', index=False)\n",
+    "train_data_orig.to_csv('normalized_train_data.csv', index=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f1e5bde8-fc56-479b-83cf-5251b070a542",
+   "metadata": {},
+   "source": [
+    "### Preliminary Data Exploration"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "f851a8b4-537b-4485-ba65-9df7f0dd266a",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 10979 entries, 0 to 10978\n",
+      "Data columns (total 22 columns):\n",
+      " #   Column   Non-Null Count  Dtype  \n",
+      "---  ------   --------------  -----  \n",
+      " 0   output   10979 non-null  float64\n",
+      " 1   input1   10979 non-null  float64\n",
+      " 2   input2   10979 non-null  float64\n",
+      " 3   input3   10979 non-null  float64\n",
+      " 4   input4   10979 non-null  float64\n",
+      " 5   input5   10979 non-null  float64\n",
+      " 6   input6   10979 non-null  float64\n",
+      " 7   input7   10979 non-null  float64\n",
+      " 8   input8   10979 non-null  float64\n",
+      " 9   input9   10979 non-null  float64\n",
+      " 10  input10  10979 non-null  float64\n",
+      " 11  input11  10979 non-null  float64\n",
+      " 12  input12  10979 non-null  float64\n",
+      " 13  input13  10979 non-null  float64\n",
+      " 14  input14  10979 non-null  float64\n",
+      " 15  input15  10979 non-null  float64\n",
+      " 16  input16  10979 non-null  float64\n",
+      " 17  input17  10979 non-null  float64\n",
+      " 18  input18  10979 non-null  float64\n",
+      " 19  input19  10979 non-null  float64\n",
+      " 20  input20  10979 non-null  float64\n",
+      " 21  input21  10979 non-null  float64\n",
+      "dtypes: float64(22)\n",
+      "memory usage: 1.8 MB\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "output     0\n",
+       "input1     0\n",
+       "input2     0\n",
+       "input3     0\n",
+       "input4     0\n",
+       "input5     0\n",
+       "input6     0\n",
+       "input7     0\n",
+       "input8     0\n",
+       "input9     0\n",
+       "input10    0\n",
+       "input11    0\n",
+       "input12    0\n",
+       "input13    0\n",
+       "input14    0\n",
+       "input15    0\n",
+       "input16    0\n",
+       "input17    0\n",
+       "input18    0\n",
+       "input19    0\n",
+       "input20    0\n",
+       "input21    0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Visualize first rows of the data\n",
+    "train_data.head()\n",
+    "\n",
+    "# Obtain summary info about data\n",
+    "train_data.info()\n",
+    "\n",
+    "# Statistical summary\n",
+    "train_data.describe()\n",
+    "\n",
+    "#Check for null or missing values and handle them.\n",
+    "data.isnull().sum()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "074a38d4-ac5b-4126-9817-4391f4401609",
+   "metadata": {},
+   "source": [
+    "### Detailed Data Exploration and Visualization"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "73ebc06f-9b59-4531-ba26-b0f07f0b7c92",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requirement already satisfied: matplotlib in /opt/conda/lib/python3.10/site-packages (3.8.0)\n",
+      "Requirement already satisfied: seaborn in /opt/conda/lib/python3.10/site-packages (0.13.0)\n",
+      "Requirement already satisfied: contourpy>=1.0.1 in /opt/conda/lib/python3.10/site-packages (from matplotlib) (1.1.1)\n",
+      "Requirement already satisfied: cycler>=0.10 in /opt/conda/lib/python3.10/site-packages (from matplotlib) (0.12.0)\n",
+      "Requirement already satisfied: fonttools>=4.22.0 in /opt/conda/lib/python3.10/site-packages (from matplotlib) (4.43.0)\n",
+      "Requirement already satisfied: kiwisolver>=1.0.1 in /opt/conda/lib/python3.10/site-packages (from matplotlib) (1.4.5)\n",
+      "Requirement already satisfied: numpy<2,>=1.21 in /opt/conda/lib/python3.10/site-packages (from matplotlib) (1.26.0)\n",
+      "Requirement already satisfied: packaging>=20.0 in /opt/conda/lib/python3.10/site-packages (from matplotlib) (23.2)\n",
+      "Requirement already satisfied: pillow>=6.2.0 in /opt/conda/lib/python3.10/site-packages (from matplotlib) (10.0.1)\n",
+      "Requirement already satisfied: pyparsing>=2.3.1 in /opt/conda/lib/python3.10/site-packages (from matplotlib) (3.1.1)\n",
+      "Requirement already satisfied: python-dateutil>=2.7 in /opt/conda/lib/python3.10/site-packages (from matplotlib) (2.8.2)\n",
+      "Requirement already satisfied: pandas>=1.2 in /opt/conda/lib/python3.10/site-packages (from seaborn) (2.1.1)\n",
+      "Requirement already satisfied: pytz>=2020.1 in /opt/conda/lib/python3.10/site-packages (from pandas>=1.2->seaborn) (2023.3)\n",
+      "Requirement already satisfied: tzdata>=2022.1 in /opt/conda/lib/python3.10/site-packages (from pandas>=1.2->seaborn) (2023.3)\n",
+      "Requirement already satisfied: six>=1.5 in /opt/conda/lib/python3.10/site-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install matplotlib seaborn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "f6cc0667-31ad-4523-bafc-8a588fdcf8ba",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "bfecfaf0-803b-4293-9fd3-3529c00920d2",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "train_data = pd.read_csv('normalized_train_data.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "id": "d1cb4f55-e647-4c11-bd99-0c3fa2a4e8e4",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(10, 6))\n",
+    "sns.scatterplot(x='input21', y='output', data=train_data)\n",
+    "plt.title('Scatter plot of input21 vs output')\n",
+    "plt.xlabel('input21')\n",
+    "plt.ylabel('output')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "id": "824f2d43-94ea-46bf-af52-87f261d3119a",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(10, 6))\n",
+    "sns.barplot(x='input1', y='output', data=train_data)\n",
+    "#plt.title('Average of input1 for each category in output')\n",
+    "plt.xlabel('input1')\n",
+    "plt.ylabel('Average of output')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "db81c7e9-fb3f-42e9-853c-2c2697791f26",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/normalized_test_data.csv b/normalized_test_data.csv
new file mode 100644
index 0000000..cad02eb
--- /dev/null
+++ b/normalized_test_data.csv
@@ -0,0 +1,3338 @@
+output,input1,input2,input3,input4,input5,input6,input7,input8,input9,input10,input11,input12,input13,input14,input15,input16,input17,input18,input19,input20,input21
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diff --git a/normalized_train_data.csv b/normalized_train_data.csv
new file mode 100644
index 0000000..7ae885b
--- /dev/null
+++ b/normalized_train_data.csv
@@ -0,0 +1,7643 @@
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