MRamya-sri/Data-Preprocessing-ML

This repository contains preprocessing steps that are crucial for preparing raw data to be used effectively in machine learning models.

Data Preprocessing in Machine Learning

This repository contains notebooks and resources for data preprocessing in machine learning. The preprocessing steps are crucial for preparing raw data to be used effectively in machine learning models. This project includes two main notebooks:

• data-preprocessing.ipynb
• split-and-predicting.ipynb

Table of Contents

1. Introduction
2. Data Preprocessing
   • Loading Data
   • Handling Missing Values
   • Encoding Categorical Data
   • Feature Scaling
3. Split and Predicting
   • Splitting the Dataset
   • Training the Model
   • Making Predictions
   • Evaluating the Model
4. Conclusion
5. How to Use
6. Dependencies

Introduction

Data preprocessing is a critical step in the machine learning pipeline. This project demonstrates how to preprocess data and prepare it for machine learning tasks. It covers handling missing values, encoding categorical variables, feature scaling, and splitting data for training and testing.

Data Preprocessing

Loading Data

The first step in data preprocessing is loading the dataset. This is demonstrated in the data-preprocessing.ipynb notebook.

Handling Missing Values

Handling missing data is essential to ensure the quality of the dataset. Common methods include removing or imputing missing values. This step is also covered in the data-preprocessing.ipynb notebook.

Encoding Categorical Data

Many machine learning algorithms require numerical input. Therefore, categorical data needs to be encoded into numerical values. Various techniques such as one-hot encoding are used for this purpose.

Feature Scaling

Feature scaling is performed to standardize the range of independent variables or features of data. This is important for algorithms that compute distances between data points, like k-nearest neighbors.

Split and Predicting

Splitting the Dataset

The dataset is split into training and testing sets to evaluate the performance of the machine learning model. This is demonstrated in the split-and-predicting.ipynb notebook.

Training the Model

Once the data is preprocessed and split, the next step is to train a machine learning model on the training set.

Making Predictions

After training the model, predictions are made on the test set to evaluate the model's performance.

Evaluating the Model

Model evaluation metrics are calculated to assess the performance of the machine learning model. Common metrics include accuracy, precision, recall, and F1 score.

Conclusion

This project demonstrates the essential steps in data preprocessing and preparing data for machine learning tasks. Proper data preprocessing leads to improved model performance and more accurate predictions.

How to Use

1. Clone the repository:

   git clone https://github.com/MRamya-sri/Data-Preprocessing-ML.git

2. Navigate to the project directory:

   cd Data-Preprocessing-ML

3. Open the notebooks in Jupyter:

   jupyter notebook data-preprocessing.ipynb
   jupyter notebook split-and-predicting.ipynb

Dependencies

• Python 3.x
• Jupyter Notebook
• NumPy
• pandas
• scikit-learn