salaryregression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler, LabelEncoder, OneHotEncoder\n",
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RowNumber</th>\n",
       "      <th>CustomerId</th>\n",
       "      <th>Surname</th>\n",
       "      <th>CreditScore</th>\n",
       "      <th>Geography</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Tenure</th>\n",
       "      <th>Balance</th>\n",
       "      <th>NumOfProducts</th>\n",
       "      <th>HasCrCard</th>\n",
       "      <th>IsActiveMember</th>\n",
       "      <th>EstimatedSalary</th>\n",
       "      <th>Exited</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>15634602</td>\n",
       "      <td>Hargrave</td>\n",
       "      <td>619</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>42</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>101348.88</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>15647311</td>\n",
       "      <td>Hill</td>\n",
       "      <td>608</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Female</td>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "      <td>83807.86</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>112542.58</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>15619304</td>\n",
       "      <td>Onio</td>\n",
       "      <td>502</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>42</td>\n",
       "      <td>8</td>\n",
       "      <td>159660.80</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113931.57</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>15701354</td>\n",
       "      <td>Boni</td>\n",
       "      <td>699</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>93826.63</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>15737888</td>\n",
       "      <td>Mitchell</td>\n",
       "      <td>850</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Female</td>\n",
       "      <td>43</td>\n",
       "      <td>2</td>\n",
       "      <td>125510.82</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>79084.10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   RowNumber  CustomerId   Surname  CreditScore Geography  Gender  Age  \\\n",
       "0          1    15634602  Hargrave          619    France  Female   42   \n",
       "1          2    15647311      Hill          608     Spain  Female   41   \n",
       "2          3    15619304      Onio          502    France  Female   42   \n",
       "3          4    15701354      Boni          699    France  Female   39   \n",
       "4          5    15737888  Mitchell          850     Spain  Female   43   \n",
       "\n",
       "   Tenure    Balance  NumOfProducts  HasCrCard  IsActiveMember  \\\n",
       "0       2       0.00              1          1               1   \n",
       "1       1   83807.86              1          0               1   \n",
       "2       8  159660.80              3          1               0   \n",
       "3       1       0.00              2          0               0   \n",
       "4       2  125510.82              1          1               1   \n",
       "\n",
       "   EstimatedSalary  Exited  \n",
       "0        101348.88       1  \n",
       "1        112542.58       0  \n",
       "2        113931.57       1  \n",
       "3         93826.63       0  \n",
       "4         79084.10       0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=pd.read_csv('Churn_Modelling.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Preprocess the data\n",
    "data = data.drop(['RowNumber', 'CustomerId', 'Surname'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Encode categorical variables\n",
    "label_encoder_gender = LabelEncoder()\n",
    "data['Gender'] = label_encoder_gender.fit_transform(data['Gender'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CreditScore</th>\n",
       "      <th>Geography</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Tenure</th>\n",
       "      <th>Balance</th>\n",
       "      <th>NumOfProducts</th>\n",
       "      <th>HasCrCard</th>\n",
       "      <th>IsActiveMember</th>\n",
       "      <th>EstimatedSalary</th>\n",
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       "      <td>619</td>\n",
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       "      <td>83807.86</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>502</td>\n",
       "      <td>France</td>\n",
       "      <td>0</td>\n",
       "      <td>42</td>\n",
       "      <td>8</td>\n",
       "      <td>159660.80</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
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       "      <td>113931.57</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>699</td>\n",
       "      <td>France</td>\n",
       "      <td>0</td>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>93826.63</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>850</td>\n",
       "      <td>Spain</td>\n",
       "      <td>0</td>\n",
       "      <td>43</td>\n",
       "      <td>2</td>\n",
       "      <td>125510.82</td>\n",
       "      <td>1</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9995</th>\n",
       "      <td>771</td>\n",
       "      <td>France</td>\n",
       "      <td>1</td>\n",
       "      <td>39</td>\n",
       "      <td>5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>96270.64</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9996</th>\n",
       "      <td>516</td>\n",
       "      <td>France</td>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "      <td>10</td>\n",
       "      <td>57369.61</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>101699.77</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>9997</th>\n",
       "      <td>709</td>\n",
       "      <td>France</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>7</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>42085.58</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9998</th>\n",
       "      <td>772</td>\n",
       "      <td>Germany</td>\n",
       "      <td>1</td>\n",
       "      <td>42</td>\n",
       "      <td>3</td>\n",
       "      <td>75075.31</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>92888.52</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9999</th>\n",
       "      <td>792</td>\n",
       "      <td>France</td>\n",
       "      <td>0</td>\n",
       "      <td>28</td>\n",
       "      <td>4</td>\n",
       "      <td>130142.79</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>38190.78</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10000 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      CreditScore Geography  Gender  Age  Tenure    Balance  NumOfProducts  \\\n",
       "0             619    France       0   42       2       0.00              1   \n",
       "1             608     Spain       0   41       1   83807.86              1   \n",
       "2             502    France       0   42       8  159660.80              3   \n",
       "3             699    France       0   39       1       0.00              2   \n",
       "4             850     Spain       0   43       2  125510.82              1   \n",
       "...           ...       ...     ...  ...     ...        ...            ...   \n",
       "9995          771    France       1   39       5       0.00              2   \n",
       "9996          516    France       1   35      10   57369.61              1   \n",
       "9997          709    France       0   36       7       0.00              1   \n",
       "9998          772   Germany       1   42       3   75075.31              2   \n",
       "9999          792    France       0   28       4  130142.79              1   \n",
       "\n",
       "      HasCrCard  IsActiveMember  EstimatedSalary  Exited  \n",
       "0             1               1        101348.88       1  \n",
       "1             0               1        112542.58       0  \n",
       "2             1               0        113931.57       1  \n",
       "3             0               0         93826.63       0  \n",
       "4             1               1         79084.10       0  \n",
       "...         ...             ...              ...     ...  \n",
       "9995          1               0         96270.64       0  \n",
       "9996          1               1        101699.77       0  \n",
       "9997          0               1         42085.58       1  \n",
       "9998          1               0         92888.52       1  \n",
       "9999          1               0         38190.78       0  \n",
       "\n",
       "[10000 rows x 11 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Geography_France</th>\n",
       "      <th>Geography_Germany</th>\n",
       "      <th>Geography_Spain</th>\n",
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       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <th>4</th>\n",
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       "      <th>9995</th>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>9996</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9997</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9998</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9999</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10000 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Geography_France  Geography_Germany  Geography_Spain\n",
       "0                  1.0                0.0              0.0\n",
       "1                  0.0                0.0              1.0\n",
       "2                  1.0                0.0              0.0\n",
       "3                  1.0                0.0              0.0\n",
       "4                  0.0                0.0              1.0\n",
       "...                ...                ...              ...\n",
       "9995               1.0                0.0              0.0\n",
       "9996               1.0                0.0              0.0\n",
       "9997               1.0                0.0              0.0\n",
       "9998               0.0                1.0              0.0\n",
       "9999               1.0                0.0              0.0\n",
       "\n",
       "[10000 rows x 3 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# One-hot encode 'Geography'\n",
    "onehot_encoder_geo = OneHotEncoder(handle_unknown='ignore')\n",
    "geo_encoded = onehot_encoder_geo.fit_transform(data[['Geography']]).toarray()\n",
    "geo_encoded_df = pd.DataFrame(geo_encoded, columns=onehot_encoder_geo.get_feature_names_out(['Geography']))\n",
    "geo_encoded_df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "      <td>619</td>\n",
       "      <td>0</td>\n",
       "      <td>42</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>101348.88</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>608</td>\n",
       "      <td>0</td>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "      <td>83807.86</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>112542.58</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>502</td>\n",
       "      <td>0</td>\n",
       "      <td>42</td>\n",
       "      <td>8</td>\n",
       "      <td>159660.80</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113931.57</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>699</td>\n",
       "      <td>0</td>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>93826.63</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>850</td>\n",
       "      <td>0</td>\n",
       "      <td>43</td>\n",
       "      <td>2</td>\n",
       "      <td>125510.82</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>79084.10</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CreditScore  Gender  Age  Tenure    Balance  NumOfProducts  HasCrCard  \\\n",
       "0          619       0   42       2       0.00              1          1   \n",
       "1          608       0   41       1   83807.86              1          0   \n",
       "2          502       0   42       8  159660.80              3          1   \n",
       "3          699       0   39       1       0.00              2          0   \n",
       "4          850       0   43       2  125510.82              1          1   \n",
       "\n",
       "   IsActiveMember  EstimatedSalary  Exited  Geography_France  \\\n",
       "0               1        101348.88       1               1.0   \n",
       "1               1        112542.58       0               0.0   \n",
       "2               0        113931.57       1               1.0   \n",
       "3               0         93826.63       0               1.0   \n",
       "4               1         79084.10       0               0.0   \n",
       "\n",
       "   Geography_Germany  Geography_Spain  \n",
       "0                0.0              0.0  \n",
       "1                0.0              1.0  \n",
       "2                0.0              0.0  \n",
       "3                0.0              0.0  \n",
       "4                0.0              1.0  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Combine one-hot encoded columns with original data\n",
    "data = pd.concat([data.drop('Geography', axis=1), geo_encoded_df], axis=1)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the data into features and target\n",
    "X = data.drop('EstimatedSalary', axis=1)\n",
    "y = data['EstimatedSalary']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Split the data in training and tetsing sets\n",
    "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2,random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Scale these features\n",
    "scaler=StandardScaler()\n",
    "X_train=scaler.fit_transform(X_train)\n",
    "X_test=scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save the encoders and scaler for later use\n",
    "with open('label_encoder_gender.pkl', 'wb') as file:\n",
    "    pickle.dump(label_encoder_gender, file)\n",
    "\n",
    "with open('onehot_encoder_geo.pkl', 'wb') as file:\n",
    "    pickle.dump(onehot_encoder_geo, file)\n",
    "\n",
    "with open('scaler.pkl', 'wb') as file:\n",
    "    pickle.dump(scaler, file)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ANN Regression Problem statement"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From f:\\Udemy course\\ANN implementation\\venv1\\Lib\\site-packages\\keras\\src\\losses.py:2976: The name tf.losses.sparse_softmax_cross_entropy is deprecated. Please use tf.compat.v1.losses.sparse_softmax_cross_entropy instead.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From f:\\Udemy course\\ANN implementation\\venv1\\Lib\\site-packages\\keras\\src\\backend.py:873: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
      "\n",
      "WARNING:tensorflow:From f:\\Udemy course\\ANN implementation\\venv1\\Lib\\site-packages\\keras\\src\\optimizers\\__init__.py:309: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
      "\n",
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " dense (Dense)               (None, 64)                832       \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 32)                2080      \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 1)                 33        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 2945 (11.50 KB)\n",
      "Trainable params: 2945 (11.50 KB)\n",
      "Non-trainable params: 0 (0.00 Byte)\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Build the model\n",
    "model = Sequential([\n",
    "    Dense(64, activation='relu', input_shape=(X_train.shape[1],)),\n",
    "    Dense(32, activation='relu'),\n",
    "    Dense(1)  # Output layer for regression\n",
    "])\n",
    "\n",
    "## compile the model\n",
    "model.compile(optimizer='adam',loss='mean_absolute_error',metrics=['mae'])\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.callbacks import EarlyStopping, TensorBoard\n",
    "import datetime\n",
    "\n",
    "# Set up TensorBoard\n",
    "log_dir = \"regressionlogs/fit/\" + datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\")\n",
    "tensorboard_callback = TensorBoard(log_dir=log_dir, histogram_freq=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set up Early Stopping\n",
    "early_stopping_callback = EarlyStopping(monitor='val_loss', patience=10, restore_best_weights=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "WARNING:tensorflow:From f:\\Udemy course\\ANN implementation\\venv1\\Lib\\site-packages\\keras\\src\\utils\\tf_utils.py:492: The name tf.ragged.RaggedTensorValue is deprecated. Please use tf.compat.v1.ragged.RaggedTensorValue instead.\n",
      "\n",
      "WARNING:tensorflow:From f:\\Udemy course\\ANN implementation\\venv1\\Lib\\site-packages\\keras\\src\\engine\\base_layer_utils.py:384: The name tf.executing_eagerly_outside_functions is deprecated. Please use tf.compat.v1.executing_eagerly_outside_functions instead.\n",
      "\n",
      "250/250 [==============================] - 6s 10ms/step - loss: 100372.0156 - mae: 100372.0156 - val_loss: 98498.2500 - val_mae: 98498.2500\n",
      "Epoch 2/100\n",
      "250/250 [==============================] - 1s 4ms/step - loss: 99558.7344 - mae: 99558.7344 - val_loss: 96861.5078 - val_mae: 96861.5078\n",
      "Epoch 3/100\n",
      "250/250 [==============================] - 1s 4ms/step - loss: 96699.2656 - mae: 96699.2656 - val_loss: 92678.3906 - val_mae: 92678.3906\n",
      "Epoch 4/100\n",
      "250/250 [==============================] - 1s 4ms/step - loss: 91106.9609 - mae: 91106.9609 - val_loss: 85769.1562 - val_mae: 85769.1562\n",
      "Epoch 5/100\n",
      "250/250 [==============================] - 1s 4ms/step - loss: 82995.0938 - mae: 82995.0938 - val_loss: 76961.1250 - val_mae: 76961.1250\n",
      "Epoch 6/100\n",
      "250/250 [==============================] - 1s 4ms/step - loss: 73660.1016 - mae: 73660.1016 - val_loss: 67942.9531 - val_mae: 67942.9531\n",
      "Epoch 7/100\n",
      "250/250 [==============================] - 1s 4ms/step - loss: 64825.7344 - mae: 64825.7344 - val_loss: 60366.8711 - val_mae: 60366.8711\n",
      "Epoch 8/100\n",
      "250/250 [==============================] - 1s 4ms/step - loss: 57956.9805 - mae: 57956.9805 - val_loss: 55165.8281 - val_mae: 55165.8281\n",
      "Epoch 9/100\n",
      "250/250 [==============================] - 1s 4ms/step - loss: 53675.4844 - mae: 53675.4844 - val_loss: 52388.3906 - val_mae: 52388.3906\n",
      "Epoch 10/100\n",
      "250/250 [==============================] - 1s 4ms/step - loss: 51557.5977 - mae: 51557.5977 - val_loss: 51237.2695 - val_mae: 51237.2695\n",
      "Epoch 11/100\n",
      "250/250 [==============================] - 1s 4ms/step - loss: 50683.1172 - mae: 50683.1172 - val_loss: 50911.9727 - val_mae: 50911.9727\n",
      "Epoch 12/100\n",
      "250/250 [==============================] - 1s 4ms/step - loss: 50398.4141 - mae: 50398.4141 - val_loss: 50815.5664 - val_mae: 50815.5664\n",
      "Epoch 13/100\n",
      "250/250 [==============================] - 1s 4ms/step - loss: 50280.5391 - mae: 50280.5391 - val_loss: 50776.2852 - val_mae: 50776.2852\n",
      "Epoch 14/100\n",
      "250/250 [==============================] - 1s 4ms/step - loss: 50202.2422 - mae: 50202.2422 - val_loss: 50737.7383 - val_mae: 50737.7383\n",
      "Epoch 15/100\n",
      "250/250 [==============================] - 1s 4ms/step - loss: 50153.1719 - mae: 50153.1719 - val_loss: 50701.5391 - val_mae: 50701.5391\n",
      "Epoch 16/100\n",
      "250/250 [==============================] - 1s 4ms/step - loss: 50109.6016 - mae: 50109.6016 - val_loss: 50669.5508 - val_mae: 50669.5508\n",
      "Epoch 17/100\n",
      "250/250 [==============================] - 1s 4ms/step - loss: 50066.1211 - mae: 50066.1211 - val_loss: 50634.0156 - val_mae: 50634.0156\n",
      "Epoch 18/100\n",
      "250/250 [==============================] - 1s 4ms/step - loss: 50032.1680 - mae: 50032.1680 - val_loss: 50603.7695 - val_mae: 50603.7695\n",
      "Epoch 19/100\n",
      "250/250 [==============================] - 1s 4ms/step - loss: 49994.1406 - mae: 49994.1406 - val_loss: 50580.2852 - val_mae: 50580.2852\n",
      "Epoch 20/100\n",
      "250/250 [==============================] - 1s 4ms/step - loss: 49963.4766 - mae: 49963.4766 - val_loss: 50562.4062 - val_mae: 50562.4062\n",
      "Epoch 21/100\n",
      "250/250 [==============================] - 1s 4ms/step - loss: 49934.6719 - mae: 49934.6719 - val_loss: 50539.5742 - val_mae: 50539.5742\n",
      "Epoch 22/100\n",
      "250/250 [==============================] - 1s 4ms/step - loss: 49906.9805 - mae: 49906.9805 - val_loss: 50516.5039 - val_mae: 50516.5039\n",
      "Epoch 23/100\n",
      "250/250 [==============================] - 1s 4ms/step - loss: 49882.2891 - mae: 49882.2891 - val_loss: 50491.1719 - val_mae: 50491.1719\n",
      "Epoch 24/100\n",
      "250/250 [==============================] - 1s 4ms/step - loss: 49854.8594 - mae: 49854.8594 - val_loss: 50477.0625 - val_mae: 50477.0625\n",
      "Epoch 25/100\n",
      "250/250 [==============================] - 1s 4ms/step - loss: 49832.1250 - mae: 49832.1250 - val_loss: 50449.6094 - val_mae: 50449.6094\n",
      "Epoch 26/100\n",
      "250/250 [==============================] - 1s 4ms/step - loss: 49812.9375 - mae: 49812.9375 - val_loss: 50430.1172 - val_mae: 50430.1172\n",
      "Epoch 27/100\n",
      "250/250 [==============================] - 1s 4ms/step - loss: 49788.8398 - mae: 49788.8398 - val_loss: 50422.3945 - val_mae: 50422.3945\n",
      "Epoch 28/100\n",
      "250/250 [==============================] - 1s 4ms/step - loss: 49767.6992 - mae: 49767.6992 - val_loss: 50411.0234 - val_mae: 50411.0234\n",
      "Epoch 29/100\n",
      "250/250 [==============================] - 1s 4ms/step - loss: 49751.2656 - mae: 49751.2656 - val_loss: 50402.5977 - val_mae: 50402.5977\n",
      "Epoch 30/100\n",
      "250/250 [==============================] - 1s 4ms/step - loss: 49734.3555 - mae: 49734.3555 - val_loss: 50392.7422 - val_mae: 50392.7422\n",
      "Epoch 31/100\n",
      "250/250 [==============================] - 1s 4ms/step - loss: 49714.8203 - mae: 49714.8203 - val_loss: 50370.9805 - val_mae: 50370.9805\n",
      "Epoch 32/100\n",
      "250/250 [==============================] - 1s 5ms/step - loss: 49701.6953 - mae: 49701.6953 - val_loss: 50362.4414 - val_mae: 50362.4414\n",
      "Epoch 33/100\n",
      "250/250 [==============================] - 2s 6ms/step - loss: 49687.6250 - mae: 49687.6250 - val_loss: 50349.9766 - val_mae: 50349.9766\n",
      "Epoch 34/100\n",
      "250/250 [==============================] - 1s 5ms/step - loss: 49671.1562 - mae: 49671.1562 - val_loss: 50348.5430 - val_mae: 50348.5430\n",
      "Epoch 35/100\n",
      "250/250 [==============================] - 2s 7ms/step - loss: 49657.2266 - mae: 49657.2266 - val_loss: 50343.1719 - val_mae: 50343.1719\n",
      "Epoch 36/100\n",
      "250/250 [==============================] - 2s 7ms/step - loss: 49644.5742 - mae: 49644.5742 - val_loss: 50330.0156 - val_mae: 50330.0156\n",
      "Epoch 37/100\n",
      "250/250 [==============================] - 1s 4ms/step - loss: 49632.4883 - mae: 49632.4883 - val_loss: 50333.5156 - val_mae: 50333.5156\n",
      "Epoch 38/100\n",
      "250/250 [==============================] - 2s 8ms/step - loss: 49618.8281 - mae: 49618.8281 - val_loss: 50325.4062 - val_mae: 50325.4062\n",
      "Epoch 39/100\n",
      "250/250 [==============================] - 1s 6ms/step - loss: 49608.8672 - mae: 49608.8672 - val_loss: 50317.8125 - val_mae: 50317.8125\n",
      "Epoch 40/100\n",
      "250/250 [==============================] - 1s 5ms/step - loss: 49598.1172 - mae: 49598.1172 - val_loss: 50319.4492 - val_mae: 50319.4492\n",
      "Epoch 41/100\n",
      "250/250 [==============================] - 2s 6ms/step - loss: 49590.8281 - mae: 49590.8281 - val_loss: 50322.5117 - val_mae: 50322.5117\n",
      "Epoch 42/100\n",
      "250/250 [==============================] - 1s 5ms/step - loss: 49580.6055 - mae: 49580.6055 - val_loss: 50318.5586 - val_mae: 50318.5586\n",
      "Epoch 43/100\n",
      "250/250 [==============================] - 2s 7ms/step - loss: 49568.2422 - mae: 49568.2422 - val_loss: 50318.5195 - val_mae: 50318.5195\n",
      "Epoch 44/100\n",
      "250/250 [==============================] - 1s 5ms/step - loss: 49558.4336 - mae: 49558.4336 - val_loss: 50319.2617 - val_mae: 50319.2617\n",
      "Epoch 45/100\n",
      "250/250 [==============================] - 2s 7ms/step - loss: 49551.3242 - mae: 49551.3242 - val_loss: 50326.7852 - val_mae: 50326.7852\n",
      "Epoch 46/100\n",
      "250/250 [==============================] - 1s 5ms/step - loss: 49539.5664 - mae: 49539.5664 - val_loss: 50308.4609 - val_mae: 50308.4609\n",
      "Epoch 47/100\n",
      "250/250 [==============================] - 1s 5ms/step - loss: 49534.5117 - mae: 49534.5117 - val_loss: 50317.4570 - val_mae: 50317.4570\n",
      "Epoch 48/100\n",
      "250/250 [==============================] - 1s 5ms/step - loss: 49524.3359 - mae: 49524.3359 - val_loss: 50314.9727 - val_mae: 50314.9727\n",
      "Epoch 49/100\n",
      "250/250 [==============================] - 1s 5ms/step - loss: 49518.5703 - mae: 49518.5703 - val_loss: 50317.1875 - val_mae: 50317.1875\n",
      "Epoch 50/100\n",
      "250/250 [==============================] - 1s 5ms/step - loss: 49508.3203 - mae: 49508.3203 - val_loss: 50308.5508 - val_mae: 50308.5508\n",
      "Epoch 51/100\n",
      "250/250 [==============================] - 1s 4ms/step - loss: 49504.5352 - mae: 49504.5352 - val_loss: 50302.6953 - val_mae: 50302.6953\n",
      "Epoch 52/100\n",
      "250/250 [==============================] - 1s 4ms/step - loss: 49499.8086 - mae: 49499.8086 - val_loss: 50298.2656 - val_mae: 50298.2656\n",
      "Epoch 53/100\n",
      "250/250 [==============================] - 1s 4ms/step - loss: 49491.2617 - mae: 49491.2617 - val_loss: 50303.4844 - val_mae: 50303.4844\n",
      "Epoch 54/100\n",
      "250/250 [==============================] - 1s 5ms/step - loss: 49487.3320 - mae: 49487.3320 - val_loss: 50306.3398 - val_mae: 50306.3398\n",
      "Epoch 55/100\n",
      "250/250 [==============================] - 1s 4ms/step - loss: 49480.6289 - mae: 49480.6289 - val_loss: 50318.4492 - val_mae: 50318.4492\n",
      "Epoch 56/100\n",
      "250/250 [==============================] - 1s 4ms/step - loss: 49473.1055 - mae: 49473.1055 - val_loss: 50324.8672 - val_mae: 50324.8672\n",
      "Epoch 57/100\n",
      "250/250 [==============================] - 1s 5ms/step - loss: 49466.3906 - mae: 49466.3906 - val_loss: 50304.0000 - val_mae: 50304.0000\n",
      "Epoch 58/100\n",
      "250/250 [==============================] - 2s 6ms/step - loss: 49464.3984 - mae: 49464.3984 - val_loss: 50322.6758 - val_mae: 50322.6758\n",
      "Epoch 59/100\n",
      "250/250 [==============================] - 1s 4ms/step - loss: 49455.9961 - mae: 49455.9961 - val_loss: 50314.8945 - val_mae: 50314.8945\n",
      "Epoch 60/100\n",
      "250/250 [==============================] - 2s 7ms/step - loss: 49452.7930 - mae: 49452.7930 - val_loss: 50313.4805 - val_mae: 50313.4805\n",
      "Epoch 61/100\n",
      "250/250 [==============================] - 2s 6ms/step - loss: 49447.9961 - mae: 49447.9961 - val_loss: 50310.2852 - val_mae: 50310.2852\n",
      "Epoch 62/100\n",
      "250/250 [==============================] - 2s 8ms/step - loss: 49443.3477 - mae: 49443.3477 - val_loss: 50317.8086 - val_mae: 50317.8086\n"
     ]
    }
   ],
   "source": [
    "# Train the model\n",
    "history = model.fit(\n",
    "    X_train, y_train,\n",
    "    validation_data=(X_test, y_test),\n",
    "    epochs=100,\n",
    "    callbacks=[early_stopping_callback, tensorboard_callback]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext tensorboard"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Reusing TensorBoard on port 6006 (pid 14112), started 0:01:21 ago. (Use '!kill 14112' to kill it.)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "      <iframe id=\"tensorboard-frame-f3085f737d76bfdc\" width=\"100%\" height=\"800\" frameborder=\"0\">\n",
       "      </iframe>\n",
       "      <script>\n",
       "        (function() {\n",
       "          const frame = document.getElementById(\"tensorboard-frame-f3085f737d76bfdc\");\n",
       "          const url = new URL(\"http://localhost\");\n",
       "          const port = 6006;\n",
       "          if (port) {\n",
       "            url.port = port;\n",
       "          }\n",
       "          frame.src = url;\n",
       "        })();\n",
       "      </script>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%tensorboard --logdir regressionlogs/fit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "63/63 [==============================] - 0s 2ms/step - loss: 50298.2656 - mae: 50298.2656\n",
      "Test MAE : 50298.265625\n"
     ]
    }
   ],
   "source": [
    "## Evaluate model on the test data\n",
    "test_loss,test_mae=model.evaluate(X_test,y_test)\n",
    "print(f'Test MAE : {test_mae}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "f:\\Udemy course\\ANN implementation\\venv1\\Lib\\site-packages\\keras\\src\\engine\\training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n",
      "  saving_api.save_model(\n"
     ]
    }
   ],
   "source": [
    "model.save('regression_model.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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