/Spark-Foundation-Intership-Projects

Primary LanguageJupyter Notebook

Machine Learning Projects

This repository contains various machine learning projects that I have worked on. Each project focuses on different aspects of machine learning, from exploratory data analysis (EDA) to model development and deployment. Below is a brief description of each project.

Projects Overview

1. Decision Tree Classifier for IRIS Dataset (DTclassifier.ipynb)

  • Description: This project involves building a decision tree classifier to classify the iris flowers into three species based on the famous IRIS dataset.
  • Key Concepts: Decision Trees, Classification, Data Preprocessing, Model Evaluation.

2. EDA on Retail Store Dataset (EDA RetailStore)

  • Description: This project focuses on performing exploratory data analysis on a retail store dataset to uncover insights and patterns.
  • Key Concepts: Data Visualization, Statistical Analysis, Data Cleaning, Insight Extraction.

3. EDA on Sports Dataset (EDA-Sports.ipynb)

  • Description: In this project, I perform exploratory data analysis on a sports dataset to understand various trends and statistics.
  • Key Concepts: Data Visualization, Statistical Analysis, Data Cleaning, Insight Extraction.

4. KMeans Visualization (KMeansVisualization)

  • Description: This project involves implementing and visualizing the K-Means clustering algorithm on a dataset.
  • Key Concepts: Clustering, K-Means Algorithm, Data Visualization, Elbow Method.

5. Predict Percentage of Marks of Students (predict PercentageOfMarks of students)

  • Description: In this project, I build a regression model to predict the percentage of marks that students are likely to score based on the number of hours they study.
  • Key Concepts: Regression Analysis, Data Preprocessing, Model Evaluation.

6. Stock Market Prediction (Stock Market Prediction)

  • Description: This project involves predicting stock market prices using machine learning algorithms.
  • Key Concepts: Time Series Analysis, Regression, Data Preprocessing, Model Evaluation.

7. Terrorist Analysis (TerroristAnalysis.py)

  • Description: This project focuses on analyzing terrorist activities to uncover patterns and trends using various data analysis techniques.
  • Key Concepts: Data Analysis, Visualization, Statistical Analysis.

How to Run the Projects

Prerequisites

  • Python 3.8
  • Jupyter Notebook (for .ipynb files)
  • Required Python libraries (listed in requirements.txt or in each notebook)

Installation

  1. Clone the repository:

    git clone https://github.com/KamelSenfro/Spark-Foundation-Intership-Projects/tree/main.git
cd ml-projects

  2. Install the required libraries:

    Using pip:

    pip install -r requirements.txt

    Or manually install the libraries:

    pip install numpy pandas matplotlib seaborn scikit-learn

Running the Notebooks

  1. Start Jupyter Notebook:

    jupyter notebook

  2. Open the desired notebook and run the cells:

    Navigate to the respective .ipynb file (e.g., DTclassifier.ipynb) and run the cells to see the results.

Running Python Scripts

For the TerroristAnalysis.py script, you can run it directly from the command line:

python TerroristAnalysis.py