College Placement Prediction

Overview

This project involves predicting college placement outcomes using machine learning algorithms. The dataset, collegePlace.csv, includes various features related to students and their placement status. The goal is to build and evaluate classification models to predict whether a student will be placed based on their features.

Features

• Gender: Gender of the student.
• Stream: Academic stream of the student.
• Age: Age of the student (commented out in the code).
• Hostel: Hostel status of the student (commented out in the code).
• PlacedOrNot: Target variable indicating whether the student has been placed (1) or not (0).

Installation

Dependencies

• Python 3.x
• Pandas
• NumPy
• Matplotlib
• Seaborn
• Scikit-learn

Setup

1. Clone the Repository

   bash git clone https://github.com/yourusername/college-placement-prediction.git cd college-placement-prediction

2. Install Required Packages

   It is recommended to use a virtual environment:

   bash python -m venv venv source venv/bin/activate # On Windows use `venv\Scripts\activate`

   Install the necessary packages:

   bash pip install pandas numpy matplotlib seaborn scikit-learn

3. Download the Dataset

   Ensure the dataset collegePlace.csv is in the project directory.

Usage

1. Load and Explore the Data

   ```python import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns %matplotlib inline

   df = pd.read_csv('collegePlace.csv') df.shape df.head() df.describe() df.info() df.isnull().sum() ```

2. Preprocess the Data

   ```python from sklearn import preprocessing

   le = preprocessing.LabelEncoder() df['Gender'] = le.fittransform(df['Gender']) df['Stream'] = le.fittransform(df['Stream']) ```

3. Visualize the Data

   python sns.pairplot(df) tc = df.corr() sns.heatmap(tc)

4. Prepare Data for Modeling

   python x = df.drop(columns=['PlacedOrNot', 'Hostel']) y = df['PlacedOrNot']

5. Split the Data

   ```python from sklearn.modelselection import traintest_split

   xtrain, xtest, ytrain, ytest = traintestsplit(x, y, testsize=0.3, randomstate=3) ```

6. Train and Evaluate Models

   Decision Tree Classifier

   ```python from sklearn.tree import DecisionTreeClassifier from sklearn import metrics

   clf = DecisionTreeClassifier() clf = clf.fit(xtrain, ytrain) ypred = clf.predict(xtest)

   cm = metrics.confusionmatrix(ytest, ypred) acc = metrics.accuracyscore(ytest, ypred) pre = metrics.precisionscore(ytest, ypred) re = metrics.recallscore(ytest, ypred) f1 = metrics.f1score(ytest, y_pred) ```

   Random Forest Classifier

   ```python from sklearn.ensemble import RandomForestClassifier

   classifier = RandomForestClassifier(nestimators=1000) classifier.fit(xtrain, ytrain) ypredrf = classifier.predict(xtest)

   cmrf = confusionmatrix(ytest, ypredrf) accrf = metrics.accuracyscore(ytest, ypredrf) prerf = metrics.precisionscore(ytest, ypred_rf) ```

Results

• Decision Tree Classifier:

  • Accuracy: acc
  • Precision: pre
  • Recall: re
  • F1 Score: f1

• Random Forest Classifier:

  • Accuracy: acc_rf
  • Precision: pre_rf

Contributing

Contributions are welcome! Please fork the repository and submit a pull request with your changes. Ensure that your code adheres to the project's coding standards and includes appropriate tests.

License

This project is licensed under the MIT License. See the LICENSE file for details.

Acknowledgements

• Scikit-learn
• Pandas
• Matplotlib
• Seaborn