/Python-Machine-Learning-Project

This project was a task of university which in on Python, Pandas, Numpy, ML Basic

Primary LanguageJupyter Notebook

Python Machine Learning Project

This repository contains a machine learning project that focuses on the development of classification-based models using Python, Pandas, NumPy, and basic machine learning techniques.

Project Overview

The goal of this project is to develop classification models using real-world data. The project requirements include:

  • Using Python programming language
  • Implementing classification-based models
  • Working with a real dataset with a minimum of 2000 instances
  • Performing data preprocessing, including data cleaning
  • Conducting exploratory data analysis
  • Implementing the following models:
    • Naive Bayes
    • K-Nearest Neighbors (KNN)
    • Decision Tree
    • Logistic Regression
    • Support Vector Machine (SVM)
    • Comparing the classifiers based on their predictive accuracy

Dataset Overview

The dataset used in this project should meet the following criteria:

  • It should be a real dataset with a minimum of 2000 instances.
  • Provide a valid URL as the data source.
  • Include a description of the dataset, explaining its features and target variable.

Data Preprocessing and Exploratory Data Analysis

This section of the project involves data preprocessing steps and exploratory data analysis. Here are the key components to cover:

  • Data preprocessing steps, such as data cleaning, handling missing values, and dealing with outliers.
  • Perform exploratory data analysis to gain insights into the dataset.
  • Utilize appropriate plotting techniques where necessary to visualize the data and its characteristics.

Model Development

In this section, the focus is on developing machine learning models. The models to be implemented are:

  • Naive Bayes
  • K-Nearest Neighbors (KNN)
  • Decision Tree
  • Logistic Regression
  • Support Vector Machine (SVM)

The development process for each model should be explained, including the necessary code and screenshots. Additionally, relevant plots should be included where necessary to visualize the results.

Discussion and Conclusion

In the final section of the project, a comparison among the different models is presented. Key components to cover include:

  • Compare the models based on their performance and predictive accuracy.
  • Include relevant plots to visualize and support the comparison.
  • Provide personal observations and draw conclusions based on the project outcomes.

Repository Structure

The repository is structured as follows:

  • IMDB_Movies: This file contains the dataset files used in the project.
  • main.ipynb: This file contains Jupyter notebooks with the code implementation and analysis.
  • README.md: This file provides an overview of the project and instructions on how to navigate the repository.

Usage

To use the code and follow along with the project, follow these steps:

  1. Clone the repository to your local machine using Git or download it as a ZIP file.
  2. Navigate to the main.ipynb directory to find the Jupyter notebooks containing the code implementation.
  3. Open the notebooks in Jupyter or any compatible environment.
  4. Run the code cells sequentially to reproduce the analysis and results.

Feel free to explore and modify the code to suit your learning or research needs.

License

The content of this repository is licensed under the MIT License. You are free to use and modify the code for educational or personal purposes. However, please note that the repository is provided "as is," without any warranty or guarantee of its accuracy or reliability.