Merge pull request #120 from poojaharihar03/main

Placement_Predictor

Placement_Predictor/README.md

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+# Placement Predictos
+
+ This folder provides the code for placement analysis and an interactive interface for analyzing and visualizing data using Python libraries such as Pandas, Seaborn, Matplotlib, and Scikit-learn.
+
+
+You can access the app at [https://placementanalysis-zgmvrc67ajweogwmgep3ta.streamlit.app/](https://placementanalysis-zgmvrc67ajweogwmgep3ta.streamlit.app/).
+
+## Introduction
+
+Data analysis is an essential part of modern decision-making processes. With the proliferation of data in various domains, having tools to explore, visualize, and understand data is crucial. This Streamlit app aims to simplify the data analysis process by providing a user-friendly interface to perform common data analysis tasks.
+Further, a Logistic Regression Model is used to find the placement of candidates and the app is hosted on streamlit
+## Features
+
+- Load and explore datasets
+- Perform data preprocessing (e.g., handling missing values, encoding categorical variables)
+- Visualize data using various plots (e.g., scatter plots, histograms, heatmaps)
+- Train and evaluate machine learning models
+
+## Installation
+```
+git clone https://github.com/yourusername/streamlit-data-analysis-app.git
+cd Placement_Predictor
+```
+
+### Create a Virtual Environment
+#### macOS
+
+Open Terminal and navigate to the project directory. Run the following commands to create a virtual environment named `venv`:
+```
+python3 -m venv venv
+```
+Activate the virtual environment:
+```
+source venv/bin/activate
+```
+
+#### Windows
+
+Open Command Prompt and navigate to the project directory. Run the following commands to create a virtual environment named `venv`:
+```
+python -m venv venv
+```
+Activate the virtual environment:
+```
+venv\Scripts\activate
+```
+
+### Install Dependencies
+
+Once the virtual environment is activated, install the required dependencies using pip:
+```
+pip install -r requirements.txt
+```
+
+## Usage
+
+Once the dependencies are installed, you can run the app using the following command:
+```
+streamlit run app.py
+```
+
+This will start a local server, and you can access the app in your web browser at `http://localhost:8501`.
+
+### Prerequisites
+
+Before proceeding, ensure you have Python installed on your system. You can download it from [python.org](https://www.python.org/downloads/).
+
+
+## Authors
+- [Pooja Harihar](https://github.com/poojaharihar03)
+
+
+

Placement_Predictor/Untitled.ipynb

@@ -0,0 +1,168 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "06ed934a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(   Sno Gender  10th % SSC Board  12th % HSC Board 12th Stream  Degree %  \\\n",
+       " 0    1      M   67.00    Others   91.00    Others    Commerce     58.00   \n",
+       " 1    2      M   79.33   Central   78.33    Others     Science     77.48   \n",
+       " 2    3      M   65.00   Central   68.00   Central        Arts     64.00   \n",
+       " 3    4      M   56.00   Central   52.00   Central     Science     52.00   \n",
+       " 4    5      M   85.80   Central   73.60   Central    Commerce     73.30   \n",
+       " \n",
+       "   Degree stream Work exp specialisation  Mba %      status    salary  \n",
+       " 0      Sci&Tech       No         Mkt&HR  58.80      Placed  270000.0  \n",
+       " 1      Sci&Tech      Yes        Mkt&Fin  66.28      Placed  200000.0  \n",
+       " 2     Comm&Mgmt       No        Mkt&Fin  57.80      Placed  250000.0  \n",
+       " 3      Sci&Tech       No         Mkt&HR  59.43  Not Placed       NaN  \n",
+       " 4     Comm&Mgmt       No        Mkt&Fin  55.50      Placed  425000.0  ,\n",
+       "    Unnamed: 0  cgpa     iq  placement\n",
+       " 0           0   6.8  123.0          1\n",
+       " 1           1   5.9  106.0          0\n",
+       " 2           2   5.3  121.0          0\n",
+       " 3           3   7.4  132.0          1\n",
+       " 4           4   5.8  142.0          0)"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "# Load datasets\n",
+    "file1 = 'datasets/Placement_Data_Full_Class.csv'\n",
+    "file2 = 'datasets/placement-dataset.csv'\n",
+    "\n",
+    "data1 = pd.read_csv(file1)\n",
+    "data2 = pd.read_csv(file2)\n",
+    "\n",
+    "# Display the first few rows of each dataset\n",
+    "data1_head = data1.head()\n",
+    "data2_head = data2.head()\n",
+    "\n",
+    "data1_head, data2_head\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "c4f906a8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  Gender  10th % SSC Board  12th % HSC Board 12th Stream  Degree %  \\\n",
+      "0      M   67.00    Others   91.00    Others    Commerce     58.00   \n",
+      "1      M   79.33   Central   78.33    Others     Science     77.48   \n",
+      "2      M   65.00   Central   68.00   Central        Arts     64.00   \n",
+      "3      M   56.00   Central   52.00   Central     Science     52.00   \n",
+      "4      M   85.80   Central   73.60   Central    Commerce     73.30   \n",
+      "\n",
+      "  Degree stream Work exp specialisation  Mba %      status    salary  cgpa  \\\n",
+      "0      Sci&Tech       No         Mkt&HR  58.80      Placed  270000.0   5.9   \n",
+      "1      Sci&Tech      Yes        Mkt&Fin  66.28      Placed  200000.0   5.3   \n",
+      "2     Comm&Mgmt       No        Mkt&Fin  57.80      Placed  250000.0   7.4   \n",
+      "3      Sci&Tech       No         Mkt&HR  59.43  Not Placed       NaN   5.8   \n",
+      "4     Comm&Mgmt       No        Mkt&Fin  55.50      Placed  425000.0   7.1   \n",
+      "\n",
+      "      iq  placement  \n",
+      "0  106.0          0  \n",
+      "1  121.0          0  \n",
+      "2  132.0          1  \n",
+      "3  142.0          0  \n",
+      "4   48.0          1  \n"
+     ]
+    }
+   ],
+   "source": [
+    "# Reset index for merging\n",
+    "data1 = data1.reset_index(drop=True)\n",
+    "data2 = data2.rename(columns={'Unnamed: 0': 'Sno'})\n",
+    "\n",
+    "# Merge on 'Sno'\n",
+    "merged_data = pd.merge(data1, data2, on='Sno', how='inner')\n",
+    "\n",
+    "# Drop unnecessary columns\n",
+    "merged_data = merged_data.drop(['Sno'], axis=1)\n",
+    "\n",
+    "# Display the first few rows of the merged dataset\n",
+    "print(merged_data.head())\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "2e1707b8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'datasets/merged_placement_data.csv'"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Merge datasets\n",
+    "data1 = data1.reset_index(drop=True)\n",
+    "data2 = data2.rename(columns={'Unnamed: 0': 'Sno'})\n",
+    "\n",
+    "# Merge on 'Sno'\n",
+    "merged_data = pd.merge(data1, data2, on='Sno', how='inner')\n",
+    "\n",
+    "# Drop unnecessary columns\n",
+    "merged_data = merged_data.drop(['Sno'], axis=1)\n",
+    "\n",
+    "# Save the merged dataset to a CSV file\n",
+    "merged_file_path = 'datasets/merged_placement_data.csv'\n",
+    "merged_data.to_csv(merged_file_path, index=False)\n",
+    "\n",
+    "merged_file_path\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7cbf15e8",
+   "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": "3.11.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}