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+.DS_STORE
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+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "from sklearn.model_selection import train_test_split, GridSearchCV, cross_val_score\n",
+ "from sklearn.preprocessing import OneHotEncoder, StandardScaler\n",
+ "from sklearn.metrics import confusion_matrix, accuracy_score\n",
+ "from imblearn.over_sampling import SMOTE\n",
+ "import numpy as np\n",
+ "import tensorflow as tf\n",
+ "from tensorflow.keras.models import Sequential\n",
+ "from tensorflow.keras.layers import Dense, Dropout, Input\n",
+ "from scikeras.wrappers import KerasClassifier\n",
+ "import os\n",
+ "\n",
+ "os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'\n",
+ "os.environ['TF_ENABLE_ONEDNN_OPTS']='0'\n",
+ "df = pd.read_csv('/Users/akshay/Desktop/ML-Crate/DataAnalyticsSalaryPrediction/Dataset/Salary Dataset.csv')\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def convert_salary(salary):\n",
+ " salary = salary.replace('₹', '').replace('$', '').replace(',', '')\n",
+ " salary = salary.replace('₹', '').replace('£', '').replace(',', '')\n",
+ " salary = salary.replace('₹', '').replace('AFN', '').replace(',', '')\n",
+ " if '/mo' in salary:\n",
+ " return float(salary.replace('/mo', '')) * 12\n",
+ " elif '/hr' in salary:\n",
+ " return float(salary.replace('/hr', '')) * 40 * 52\n",
+ " else:\n",
+ " return float(salary.replace('/yr', ''))\n",
+ "\n",
+ "df['Salary'] = df['Salary'].apply(convert_salary)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "ename": "ValueError",
+ "evalue": "Cannot cast object dtype to int64",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
+ "File \u001b[0;32m~/Desktop/ML-Crate/DataAnalyticsSalaryPrediction/venv/lib/python3.11/site-packages/pandas/core/arrays/categorical.py:591\u001b[0m, in \u001b[0;36mCategorical.astype\u001b[0;34m(self, dtype, copy)\u001b[0m\n\u001b[1;32m 590\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 591\u001b[0m new_cats \u001b[38;5;241m=\u001b[39m \u001b[43mnew_cats\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mastype\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 592\u001b[0m fill_value \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcategories\u001b[38;5;241m.\u001b[39m_na_value\n",
+ "\u001b[0;31mValueError\u001b[0m: invalid literal for int() with base 10: 'more than 0'",
+ "\nDuring handling of the above exception, another exception occurred:\n",
+ "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
+ "Cell \u001b[0;32mIn[3], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m salary_bins \u001b[38;5;241m=\u001b[39m [\u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m500000\u001b[39m, \u001b[38;5;241m1000000\u001b[39m, \u001b[38;5;241m1500000\u001b[39m, \u001b[38;5;241m2000000\u001b[39m, np\u001b[38;5;241m.\u001b[39minf]\n\u001b[1;32m 2\u001b[0m salary_labels \u001b[38;5;241m=\u001b[39m [\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmore than 0\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmore than 500k\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmore than 1000k\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmore than 1500k\u001b[39m\u001b[38;5;124m'\u001b[39m , \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmore than 2000k\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;66;03m# Assign a label to each bin\u001b[39;00m\n\u001b[0;32m----> 3\u001b[0m df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSalaryBin\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcut\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mSalary\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbins\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msalary_bins\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlabels\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msalary_labels\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mastype\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mint\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 5\u001b[0m categorical_cols \u001b[38;5;241m=\u001b[39m [\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCompany Name\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mJob Title\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mLocation\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[1;32m 6\u001b[0m X \u001b[38;5;241m=\u001b[39m df[categorical_cols]\n",
+ "File \u001b[0;32m~/Desktop/ML-Crate/DataAnalyticsSalaryPrediction/venv/lib/python3.11/site-packages/pandas/core/generic.py:6643\u001b[0m, in \u001b[0;36mNDFrame.astype\u001b[0;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[1;32m 6637\u001b[0m results \u001b[38;5;241m=\u001b[39m [\n\u001b[1;32m 6638\u001b[0m ser\u001b[38;5;241m.\u001b[39mastype(dtype, copy\u001b[38;5;241m=\u001b[39mcopy, errors\u001b[38;5;241m=\u001b[39merrors) \u001b[38;5;28;01mfor\u001b[39;00m _, ser \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mitems()\n\u001b[1;32m 6639\u001b[0m ]\n\u001b[1;32m 6641\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 6642\u001b[0m \u001b[38;5;66;03m# else, only a single dtype is given\u001b[39;00m\n\u001b[0;32m-> 6643\u001b[0m new_data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_mgr\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mastype\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 6644\u001b[0m res \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_constructor_from_mgr(new_data, axes\u001b[38;5;241m=\u001b[39mnew_data\u001b[38;5;241m.\u001b[39maxes)\n\u001b[1;32m 6645\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m res\u001b[38;5;241m.\u001b[39m__finalize__(\u001b[38;5;28mself\u001b[39m, method\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mastype\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
+ "File \u001b[0;32m~/Desktop/ML-Crate/DataAnalyticsSalaryPrediction/venv/lib/python3.11/site-packages/pandas/core/internals/managers.py:430\u001b[0m, in \u001b[0;36mBaseBlockManager.astype\u001b[0;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[1;32m 427\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m using_copy_on_write():\n\u001b[1;32m 428\u001b[0m copy \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[0;32m--> 430\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 431\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mastype\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 432\u001b[0m \u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 433\u001b[0m \u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 434\u001b[0m \u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 435\u001b[0m \u001b[43m \u001b[49m\u001b[43musing_cow\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43musing_copy_on_write\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 436\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
+ "File \u001b[0;32m~/Desktop/ML-Crate/DataAnalyticsSalaryPrediction/venv/lib/python3.11/site-packages/pandas/core/internals/managers.py:363\u001b[0m, in \u001b[0;36mBaseBlockManager.apply\u001b[0;34m(self, f, align_keys, **kwargs)\u001b[0m\n\u001b[1;32m 361\u001b[0m applied \u001b[38;5;241m=\u001b[39m b\u001b[38;5;241m.\u001b[39mapply(f, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m 362\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 363\u001b[0m applied \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mgetattr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mb\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mf\u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 364\u001b[0m result_blocks \u001b[38;5;241m=\u001b[39m extend_blocks(applied, result_blocks)\n\u001b[1;32m 366\u001b[0m out \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mtype\u001b[39m(\u001b[38;5;28mself\u001b[39m)\u001b[38;5;241m.\u001b[39mfrom_blocks(result_blocks, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maxes)\n",
+ "File \u001b[0;32m~/Desktop/ML-Crate/DataAnalyticsSalaryPrediction/venv/lib/python3.11/site-packages/pandas/core/internals/blocks.py:758\u001b[0m, in \u001b[0;36mBlock.astype\u001b[0;34m(self, dtype, copy, errors, using_cow, squeeze)\u001b[0m\n\u001b[1;32m 755\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCan not squeeze with more than one column.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 756\u001b[0m values \u001b[38;5;241m=\u001b[39m values[\u001b[38;5;241m0\u001b[39m, :] \u001b[38;5;66;03m# type: ignore[call-overload]\u001b[39;00m\n\u001b[0;32m--> 758\u001b[0m new_values \u001b[38;5;241m=\u001b[39m \u001b[43mastype_array_safe\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 760\u001b[0m new_values \u001b[38;5;241m=\u001b[39m maybe_coerce_values(new_values)\n\u001b[1;32m 762\u001b[0m refs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
+ "File \u001b[0;32m~/Desktop/ML-Crate/DataAnalyticsSalaryPrediction/venv/lib/python3.11/site-packages/pandas/core/dtypes/astype.py:237\u001b[0m, in \u001b[0;36mastype_array_safe\u001b[0;34m(values, dtype, copy, errors)\u001b[0m\n\u001b[1;32m 234\u001b[0m dtype \u001b[38;5;241m=\u001b[39m dtype\u001b[38;5;241m.\u001b[39mnumpy_dtype\n\u001b[1;32m 236\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 237\u001b[0m new_values \u001b[38;5;241m=\u001b[39m \u001b[43mastype_array\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 238\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (\u001b[38;5;167;01mValueError\u001b[39;00m, \u001b[38;5;167;01mTypeError\u001b[39;00m):\n\u001b[1;32m 239\u001b[0m \u001b[38;5;66;03m# e.g. _astype_nansafe can fail on object-dtype of strings\u001b[39;00m\n\u001b[1;32m 240\u001b[0m \u001b[38;5;66;03m# trying to convert to float\u001b[39;00m\n\u001b[1;32m 241\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m errors \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mignore\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n",
+ "File \u001b[0;32m~/Desktop/ML-Crate/DataAnalyticsSalaryPrediction/venv/lib/python3.11/site-packages/pandas/core/dtypes/astype.py:179\u001b[0m, in \u001b[0;36mastype_array\u001b[0;34m(values, dtype, copy)\u001b[0m\n\u001b[1;32m 175\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m values\n\u001b[1;32m 177\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(values, np\u001b[38;5;241m.\u001b[39mndarray):\n\u001b[1;32m 178\u001b[0m \u001b[38;5;66;03m# i.e. ExtensionArray\u001b[39;00m\n\u001b[0;32m--> 179\u001b[0m values \u001b[38;5;241m=\u001b[39m \u001b[43mvalues\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mastype\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 181\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 182\u001b[0m values \u001b[38;5;241m=\u001b[39m _astype_nansafe(values, dtype, copy\u001b[38;5;241m=\u001b[39mcopy)\n",
+ "File \u001b[0;32m~/Desktop/ML-Crate/DataAnalyticsSalaryPrediction/venv/lib/python3.11/site-packages/pandas/core/arrays/categorical.py:602\u001b[0m, in \u001b[0;36mCategorical.astype\u001b[0;34m(self, dtype, copy)\u001b[0m\n\u001b[1;32m 597\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (\n\u001b[1;32m 598\u001b[0m \u001b[38;5;167;01mTypeError\u001b[39;00m, \u001b[38;5;66;03m# downstream error msg for CategoricalIndex is misleading\u001b[39;00m\n\u001b[1;32m 599\u001b[0m \u001b[38;5;167;01mValueError\u001b[39;00m,\n\u001b[1;32m 600\u001b[0m ):\n\u001b[1;32m 601\u001b[0m msg \u001b[38;5;241m=\u001b[39m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCannot cast \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcategories\u001b[38;5;241m.\u001b[39mdtype\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m dtype to \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mdtype\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m--> 602\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(msg)\n\u001b[1;32m 604\u001b[0m result \u001b[38;5;241m=\u001b[39m take_nd(\n\u001b[1;32m 605\u001b[0m new_cats, ensure_platform_int(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_codes), fill_value\u001b[38;5;241m=\u001b[39mfill_value\n\u001b[1;32m 606\u001b[0m )\n\u001b[1;32m 608\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n",
+ "\u001b[0;31mValueError\u001b[0m: Cannot cast object dtype to int64"
+ ]
+ }
+ ],
+ "source": [
+ "salary_bins = [0, 500000, 1000000, 1500000, 2000000, np.inf]\n",
+ "salary_labels = [0, 1, 2, 3, 4] # Assign a label to each bin\n",
+ "df['SalaryBin'] = pd.cut(df['Salary'], bins=salary_bins, labels=salary_labels).astype(int)\n",
+ "\n",
+ "categorical_cols = ['Company Name', 'Job Title', 'Location']\n",
+ "X = df[categorical_cols]\n",
+ "y = df['SalaryBin']\n",
+ "\n",
+ "encoder = OneHotEncoder(sparse_output=False)\n",
+ "X_encoded = encoder.fit_transform(X)\n",
+ "\n",
+ "scaler = StandardScaler()\n",
+ "X_encoded_scaled = scaler.fit_transform(X_encoded)\n",
+ "\n",
+ "smote = SMOTE(random_state=42)\n",
+ "X_resampled, y_resampled = smote.fit_resample(X_encoded_scaled, y)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "\n",
+ "def create_model(optimizer='adam'):\n",
+ " model = Sequential()\n",
+ " model.add(Input(shape=(X_resampled.shape[1],)))\n",
+ " model.add(Dense(128, activation='relu', kernel_regularizer=tf.keras.regularizers.l2(0.01)))\n",
+ " model.add(Dropout(0.5))\n",
+ " model.add(Dense(64, activation='relu', kernel_regularizer=tf.keras.regularizers.l2(0.01)))\n",
+ " model.add(Dropout(0.5))\n",
+ " model.add(Dense(32, activation='relu', kernel_regularizer=tf.keras.regularizers.l2(0.01)))\n",
+ " model.add(Dense(5, activation='softmax'))\n",
+ " model.compile(optimizer=optimizer, loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n",
+ " return model"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "model = KerasClassifier(model=create_model, verbose=0)\n",
+ "param_grid = {\n",
+ " 'optimizer': ['adam', 'rmsprop'],\n",
+ " 'epochs': [50, 100],\n",
+ " 'batch_size': [64, 128]\n",
+ "}\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Best: 0.600000 using {'batch_size': 128, 'epochs': 100, 'optimizer': 'adam'}\n",
+ "Accuracy: 61.22% (+/- 8.57%)\n",
+ "Confusion Matrix:\n",
+ "[[195 92 62 13 8]\n",
+ " [111 134 131 22 8]\n",
+ " [ 33 38 282 12 12]\n",
+ " [ 7 13 12 350 5]\n",
+ " [ 4 11 6 11 333]]\n",
+ "Accuracy: 0.68\n"
+ ]
+ }
+ ],
+ "source": [
+ "grid = GridSearchCV(estimator=model, param_grid=param_grid, n_jobs=4, cv=3, error_score='raise')\n",
+ "grid_result = grid.fit(X_resampled, y_resampled)\n",
+ "\n",
+ "print(\"Best: %f using %s\" % (grid_result.best_score_, grid_result.best_params_))\n",
+ "best_model = grid_result.best_estimator_\n",
+ "\n",
+ "# Evaluate the best model with cross-validation\n",
+ "scores = cross_val_score(best_model, X_resampled, y_resampled, cv=5)\n",
+ "print(\"Accuracy: %.2f%% (+/- %.2f%%)\" % (scores.mean() * 100, scores.std() * 100))\n",
+ "\n",
+ "# Split the data into training and testing sets\n",
+ "X_train, X_test, y_train, y_test = train_test_split(X_resampled, y_resampled, test_size=0.25, random_state=42)\n",
+ "history = best_model.fit(X_train, y_train)\n",
+ "\n",
+ "# Use predict_proba if available, otherwise use predict\n",
+ "try:\n",
+ " y_pred_prob = best_model.predict_proba(X_test)\n",
+ " y_pred = np.argmax(y_pred_prob, axis=1)\n",
+ "except AttributeError:\n",
+ " y_pred = best_model.predict(X_test)\n",
+ "\n",
+ "# Compute confusion matrix and accuracy\n",
+ "cm = confusion_matrix(y_test, y_pred)\n",
+ "accuracy = accuracy_score(y_test, y_pred)\n",
+ "\n",
+ "print(\"Confusion Matrix:\")\n",
+ "print(cm)\n",
+ "print(f\"Accuracy: {accuracy:.2f}\")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pickle"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "model_filename = 'model1'\n",
+ "with open(model_filename, 'wb') as file:\n",
+ " pickle.dump(best_model, file)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "from sklearn.preprocessing import OneHotEncoder, StandardScaler\n",
+ "import pickle\n",
+ "\n",
+ "# Load your data\n",
+ "df = pd.read_csv('/Users/akshay/Desktop/ML-Crate/DataAnalyticsSalaryPrediction/Dataset/Salary Dataset.csv')\n",
+ "\n",
+ "def convert_salary(salary):\n",
+ " salary = salary.replace('₹', '').replace('$', '').replace(',', '')\n",
+ " salary = salary.replace('₹', '').replace('£', '').replace(',', '')\n",
+ " salary = salary.replace('₹', '').replace('AFN', '').replace(',', '')\n",
+ " if '/mo' in salary:\n",
+ " return float(salary.replace('/mo', '')) * 12\n",
+ " elif '/hr' in salary:\n",
+ " return float(salary.replace('/hr', '')) * 40 * 52\n",
+ " else:\n",
+ " return float(salary.replace('/yr', ''))\n",
+ "\n",
+ "df['Salary'] = df['Salary'].apply(convert_salary)\n",
+ "\n",
+ "salary_bins = [0, 500000, 1000000, 1500000, 2000000, np.inf]\n",
+ "salary_labels = [0, 1, 2, 3, 4]\n",
+ "df['SalaryBin'] = pd.cut(df['Salary'], bins=salary_bins, labels=salary_labels).astype(int)\n",
+ "\n",
+ "categorical_cols = ['Company Name', 'Job Title', 'Location']\n",
+ "X = df[categorical_cols]\n",
+ "y = df['SalaryBin']\n",
+ "\n",
+ "# Create and fit the encoder\n",
+ "encoder = OneHotEncoder(sparse_output=False)\n",
+ "X_encoded = encoder.fit_transform(X)\n",
+ "\n",
+ "# Create and fit the scaler\n",
+ "scaler = StandardScaler()\n",
+ "X_encoded_scaled = scaler.fit_transform(X_encoded)\n",
+ "\n",
+ "# Save the encoder\n",
+ "with open('/Users/akshay/Desktop/ML-Crate/DataAnalyticsSalaryPrediction/Model/encoder.pkl', 'wb') as file:\n",
+ " pickle.dump(encoder, file)\n",
+ "\n",
+ "# Save the scaler\n",
+ "with open('/Users/akshay/Desktop/ML-Crate/DataAnalyticsSalaryPrediction/Model/scaler.pkl', 'wb') as file:\n",
+ " pickle.dump(scaler, file)\n"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "venv",
+ "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": "3.11.4"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Salary Prediction of Data Analytics with Web App/Model/model1 b/Salary Prediction of Data Analytics with Web App/Model/model1
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+++ b/Salary Prediction of Data Analytics with Web App/Model/requirements.txt
@@ -0,0 +1,6 @@
+pandas
+numpy
+scikit-learn
+tensorflow
+matplotlib
+keras
diff --git a/Salary Prediction of Data Analytics with Web App/Model/scaler.pkl b/Salary Prediction of Data Analytics with Web App/Model/scaler.pkl
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diff --git a/Salary Prediction of Data Analytics with Web App/Web App/app.py b/Salary Prediction of Data Analytics with Web App/Web App/app.py
new file mode 100644
index 000000000..18f9c8945
--- /dev/null
+++ b/Salary Prediction of Data Analytics with Web App/Web App/app.py
@@ -0,0 +1,56 @@
+import os
+import pickle
+import numpy as np
+from flask import Flask, request, jsonify, render_template
+from sklearn.preprocessing import OneHotEncoder, StandardScaler
+import pandas as pd
+import tensorflow as tf
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import Dense, Dropout, Input
+
+
+app = Flask(__name__)
+
+def create_model(optimizer='adam'):
+ model = Sequential()
+ model.add(Input(shape=(X_resampled.shape[1],))) # Assuming X_resampled.shape[1] is known
+ model.add(Dense(128, activation='relu', kernel_regularizer=tf.keras.regularizers.l2(0.01)))
+ model.add(Dropout(0.5))
+ model.add(Dense(64, activation='relu', kernel_regularizer=tf.keras.regularizers.l2(0.01)))
+ model.add(Dropout(0.5))
+ model.add(Dense(32, activation='relu', kernel_regularizer=tf.keras.regularizers.l2(0.01)))
+ model.add(Dense(5, activation='softmax'))
+ model.compile(optimizer=optimizer, loss='sparse_categorical_crossentropy', metrics=['accuracy'])
+ return model
+
+# Load the trained model
+with open('/Users/akshay/Desktop/ML-Crate/DataAnalyticsSalaryPrediction/Model/model1', 'rb') as file:
+ model = pickle.load(file)
+
+# Load the encoder and scaler
+with open('/Users/akshay/Desktop/ML-Crate/DataAnalyticsSalaryPrediction/Model/encoder.pkl', 'rb') as file:
+ encoder = pickle.load(file)
+
+with open('/Users/akshay/Desktop/ML-Crate/DataAnalyticsSalaryPrediction/Model/scaler.pkl', 'rb') as file:
+ scaler = pickle.load(file)
+
+# Preprocessing function
+def preprocess_input(data):
+ df = pd.DataFrame(data, index=[0])
+ df_encoded = encoder.transform(df)
+ df_scaled = scaler.transform(df_encoded)
+ return df_scaled
+
+@app.route('/')
+def home():
+ return render_template('index.html')
+
+@app.route('/predict', methods=['POST'])
+def predict():
+ data = request.get_json()
+ input_data = preprocess_input(data)
+ prediction = model.predict(input_data)
+ return jsonify({'prediction': int(prediction[0])})
+
+if __name__ == '__main__':
+ app.run(debug=True)
diff --git a/Salary Prediction of Data Analytics with Web App/Web App/templates/index.html b/Salary Prediction of Data Analytics with Web App/Web App/templates/index.html
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Salary Prediction
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