themrityunjaypathak Goto Github PK

𝗔𝗕𝗢𝗨𝗧     𝗦𝗞𝗜𝗟𝗟𝗦     𝗣𝗥𝗢𝗝𝗘𝗖𝗧𝗦     𝗡𝗢𝗧𝗘𝗕𝗢𝗢𝗞𝗦     𝗖𝗘𝗥𝗧𝗜𝗙𝗜𝗖𝗔𝗧𝗘𝗦     𝗔𝗖𝗛𝗜𝗘𝗩𝗘𝗠𝗘𝗡𝗧𝗦     𝗖𝗢𝗡𝗧𝗔𝗖𝗧

Hi 👋, I'm Mrityunjay Pathak

I am a dedicated Data Scientist with a knack for uncovering patterns and trends that drive smarter decisions.

🎯 Skills

• Programming : I'm skilled in using Python, a powerful language for data science and data analysis.

• Libraries : I'm familiar with essential libraries like NumPy (for numerical operations), Pandas (for data manipulation), and Matplotlib/Seaborn (for visualizing data).

• Machine Learning : I'm familiar in various machine learning techniques like Regression (predicting values), Classification (sorting data into categories), Clustering (grouping similar data), Dimensionality Reduction (simplifying complex data), and Model Selection (choosing the right predictive model).

• Database : I can work with MySQL, a popular database system, to handle and retrieve data effectively.

• BI Tool : I'm proficient in using Microsoft Power BI, a tool that helps in turning data into actionable insights through interactive visualizations.

• Version Control : I'm familiar with Git, which helps in keeping track of changes in code and collaborating effectively with a team.

• Web Application : I have experience working with Streamlit, an open-source Python library that makes it easy to create custom web apps for machine learning and data science.

• Web Development : I also have some knowledge in web development, specifically HTML and CSS.

📫 Connect with Me

• E-Mail : [email protected]

• Phone : +91 9336158993

• Resume : Mrityunjay Pathak - Resume

Python	NumPy	Pandas	Matplotlib

Seaborn	Sklearn	MySQL	Power BI

Git	Streamlit	HTML	CSS

🗃️ Machine Learning

🔍 Exploratory Data Analysis

📊 Microsoft Power BI

📕 Data Science

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• Python

• NumPy

• Pandas

• Matplotlib

For more Details → Go to Data Science Repository

📗 Machine Learning

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• Linear Regression

• Logistic Regression

• Decision Trees

• Random Forests

• Support Vector Machines (SVM)

• K-Nearest Neighbors (KNN)

• K-Means Clustering

• K-Fold Cross Validation

• Naive Bayes

• Principal Component Analysis (PCA)

• Bagging Ensemble

• Saving Model to a File

For more Details → Go to Machine Learning Repository

📘 Feature Engineering

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• Dummy Variable

• Inter Quartile Range

• Z-Score

• Modified Z-Score

• Data Standardization

• Handling Imbalance Dataset

For more Details → Go to Feature Engineering Repository

📙 Structured Query Language

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• SQL Basics

• SQL Statements

• LIMIT, DISTINCT, ORDER BY Clause

• SQL WHERE Statement

• SQL Logical Operators

• SQL Joins

• SQL Aggregation - 1

• SQL Aggregation - 2

• SQL Aggregation - 3

• SQL Sub-Queries and Window Function

For more Details → Go to SQL for Data Science Repository

📓 Machine Learning Mathematics

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• Linear Algebra

• Introduction to Matrices

• Matrix Operation using Python

• Geometry

• Distance Metrics

• Similarity Measures

• Box Cox Transformation

• Statistics

• Basics of Statistics

• Descriptive Statistics with Python

• Bias and Variance

• Covariance and Correlation

• Normal Distribution

• Hypothesis Testing

• Probability

• Introduction to Probability

• Probability Distribution

• Calculus

• Introduction to Calculus

For more Details → Go to Machine Learning Mathematics Repository

📃 Kaggle Certificates

📃 HackerRank Certificates

5 ⭐ Python HackerRank	5 ⭐ SQL HackerRank	Kaggle Master