Overview

Briefly introduce your project, emphasizing its goal and how it involves data analysis and machine learning.

Data Analysis

• Data Cleaning and Transformation:

  • Handle missing values, remove duplicates, and correct inconsistencies.
  • Normalize or scale features and convert categorical variables.

• Exploratory Data Analysis (EDA):

  • Identify patterns, trends, and outliers using statistical and visualization techniques.

• Statistical Analysis:

  • Apply tests to validate hypotheses and conduct correlation analysis.

• Data Visualization:

  • Create visual representations using plots and charts to communicate findings.

Machine Learning

• Types of Machine Learning:

  • Supervised Learning: Linear Regression, Decision Trees, Support Vector Machines, Neural Networks.
  • Unsupervised Learning: K-Means Clustering, Hierarchical Clustering, PCA.
  • Reinforcement Learning: Q-Learning, DQN.

• Steps in Machine Learning:

  • Data preprocessing, model selection, training, evaluation, hyperparameter tuning, and deployment.

Getting Started

Clone the repository

git clone https://github.com/Kishankumar1328/-data-analysics.git

Navigate to the project directory

cd your-repo

Install dependencies

pip install -r requirements.txt

Add any specific setup or configuration steps related to data analysis and machine learning components.

Usage

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Results

Share the results of your data analysis and machine learning experiments. Highlight insights gained and performance metrics.

Contributing

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License

This project is licensed under the Apache-2.0 license- see the LICENSE file for details.

Contact

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