Machine Learning Workshop

This repository contains a collection of machine learning projects and tutorials developed during a ML training workshop. The repository is organized into two main directories: Projects and Tutorials.

Projects

1. Course Predictor-

-A predictive model for course recommendations

2. House Price Predictor-

-Predicts house prices

3. Iris Predictor-

-Classifies iris flowers into species

4. Purchasing Predictor-

-Predicts customer purchase behavior

5. Student Score Predictor-

-Predicts student scores based on study hours

6. Weather Predictor-

-Weather prediction model

7. Weekly Riders Predictor-

-Predicts weekly ridership

Tutorials

Core ML Concepts-

• DataPreProcessing.ipynb - Data cleaning and preprocessing techniques
• LinearReg.ipynb - Linear regression implementation
• Logistic_Regression.ipynb - Logistic regression for classification
• DecisionTreeClassifier.ipynb - Decision tree algorithms
• SVM.ipynb - Support Vector Machines implementation
• Deep_Learning_with_tensorflow_and_keras.ipynb - Introduction to deep learning
• Compare_Model_on_Complex_Datasets.ipynb - Model comparison on complex datasets
• PracQs.ipynb - Practice questions and solutions
• python-lecture-0[1-3]-optimize-ai.ipynb - Python optimization techniques

Data Analysis & Visualization-

• Numpy.ipynb - NumPy fundamentals
• Pandas.ipynb - Data manipulation with Pandas
• Matplotlib.ipynb - Data visualization
• Numpy_and_Matplotlib.ipynb - Combined NumPy and Matplotlib examples

Setup

1. Clone the repository:

   git clone https://github.com/yourusername/ML_trainingP.git
   cd ML_trainingP

2. Install dependencies:

   pip install -r requirements.txt

3. Run the file: For Projects-

   cd Projects
   cd *project_name*
   python model.py #for creating .pkl file
   streamlit run main.py #to run the project

   For Tutorials-

   jupyter notebook Tutorials/