Briefly introduce your project, emphasizing its goal and how it involves data analysis and machine learning.
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Data Cleaning and Transformation:
- Handle missing values, remove duplicates, and correct inconsistencies.
- Normalize or scale features and convert categorical variables.
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Exploratory Data Analysis (EDA):
- Identify patterns, trends, and outliers using statistical and visualization techniques.
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Statistical Analysis:
- Apply tests to validate hypotheses and conduct correlation analysis.
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Data Visualization:
- Create visual representations using plots and charts to communicate findings.
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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.
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Steps in Machine Learning:
- Data preprocessing, model selection, training, evaluation, hyperparameter tuning, and deployment.
git clone https://github.com/Kishankumar1328/-data-analysics.git
cd your-repo
pip install -r requirements.txt
Add any specific setup or configuration steps related to data analysis and machine learning components.
Show examples of how to use your project for data analysis and machine learning. Include code snippets or example scripts.
Share the results of your data analysis and machine learning experiments. Highlight insights gained and performance metrics.
Explain how others can contribute to your project, whether it's bug reports, feature requests, or code contributions related to data analysis and machine learning.
This project is licensed under the Apache-2.0 license- see the LICENSE file for details.
Provide a way for users to reach out to you, whether it's through email, GitHub issues.