chaitanyac22 Goto Github PK

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Name: Chaitanya Chaudhari

Type: User

Bio: Machine Learning Engineer | Data Scientist

Location: San Francisco

Hi 👋🏼, I am Chaitanya Chaudhari!

Welcome to my GitHub profile 🙏🏼

About Me:

Machine Learning Engineer | Data Scientist

An enthusiastic and seasoned professional leveraging the power of data and machine learning to resolve intricate problems.

🔭 Innovating: Unleashing data alchemy as a Data Maverick.

🌱 Absorbing: Soaking up the latest data sorcery for transformative insights.

💬 Contemplating: Exploring the enigmatic realm of data's hidden symphony.

📧 Reach Out: Contact me via email at [email protected].

⚡ Zephyr-chaser: Pursuing diverse data realms with a whimsical flair!

Profile Summary

As a passionate machine learning engineer and data scientist, I am driven by the potential of technology to address complex challenges. With a strong background in statistics, machine learning, artificial intelligence, computer vision, deep learning, mathematics, and NLP, I am constantly improving my skills and deepening my understanding in these areas. I place a strong emphasis on delivering quality outcomes while recognizing the importance of continuous learning and growth.

With a focus on data science and machine learning, I am committed to making a positive difference. My portfolio, featuring my work and contributions, can be viewed on GitHub (here, under repositories) and Tableau. I am grateful for opportunities to grow as a professional and showcase my expertise and commitment to making a lasting impact. If you share my vision and wish to collaborate, feel free to reach out at 📫 [email protected]

Professional Links:

Domains of Interests & Expertise

☄️ Statistics
☄️ Data Science
☄️ Machine Learning
☄️ Artificial Intelligence
☄️ Computer Vision
☄️ Deep Learning
☄️ Mathematics
☄️ NLP

Skills

Languages, Libraries, Tools and Frameworks:

💻 Programming

💾 Database Management

📷 Image Processing

📊 Data Visualization

🤖 ML/DL Frameworks

🗣️ NLP

🤖 AI/ML Applications

📊 Data Science and ML

☁️ AWS

📜 Miscellaneous

Thank you for taking the time to visit my GitHub profile! 🙏🏼

Chaitanya Chaudhari's Projects

autistic-spectrum-disorder-asd-detection

This project aims to develop a robust classification model using test-takers' demographics and questionnaire responses from the ASD screening dataset to accurately identify individuals with Autistic Spectrum Disorder (ASD) through optimization of performance metrics.

building-a-chatbot-using-rasa-plus-deployment

Building a conversational bot (chatbot) which can help users discover restaurants across several Indian cities using RASA

car-price-prediction-model-for-an-automobile-consulting-company

The goal of this project is to build multiple linear regression models for the prediction of car prices.

chaitanyac22

Configuration Files (GitHub profile related)

deep-rl-project---maximize-total-profits-earned-by-cab-driver

The goal of this project is to build an RL-based algorithm that can help cab drivers maximize their profits by improving their decision-making process on the field. Taking long-term profit as the goal, a method is proposed based on reinforcement learning to optimize taxi driving strategies for profit maximization. This optimization problem is formulated as a Markov Decision Process i.e. MDP.

deep_learning_hand_gesture_recognition_project

The objective of this project is to recognize hand gestures using state-of-the-art neural networks.

fraud_analytics_credit_card_fraud_detection

The aim of this project is to predict fraudulent credit card transactions with the help of different machine learning models.

house-price-prediction-project-for-a-us-based-housing-company

The goal of this project is to garner data insights using data analytics to purchase houses at a price below their actual value and flip them on at a higher price. This project aims at building an effective regression model using regularization (i.e. advanced linear regression: Ridge and Lasso regression) in order to predict the actual values of prospective housing properties and decide whether to invest in them or not.

investment-analysis-for-an-asset-management-company

Data analysis to identify the best sectors, countries, and a suitable investment type for making investments.

lending-club-project---data-analysis-for-a-consumer-finance-company

Lending Club is a consumer finance company that specializes in lending various types of loans to urban customers. When the company receives a loan application, the company has to make a decision for loan approval based on the applicant’s profile. The project work aims to help the company in understanding the driving factors (or driver variables) behind loan default, i.e. the variables which are strong indicators of default. The company can utilize this knowledge for its portfolio and risk assessment.

multi-input-multi-output-mnist-image-digit-and-summed-output-classification

The goal of this project is to build a neural network that takes an MNIST handwritten digit (0-9) image and a random number (digit 0-9) as inputs and returns the predicted class label (0-9) for the input image and its addition (sum) with the input random number as summed output (range 0-18) label as outputs.

neural-network-using-numpy

Introduction to Neural Networks (Create a neural network using Numpy)

numerical_tictactoe_agent_using_reinforcement_learning

Build an RL (Reinforcement Learning) agent that learns to play Numerical Tic-Tac-Toe. The agent learns the game by Q-Learning.

syntactic-processing-pos-tagger

NLP: HMMs and Viterbi algorithm for POS tagging

telecom-churn-prediction

In this project, data analytics is used to analyze customer-level data of a leading telecom firm, build predictive models to identify customers at high risk of churn, and identify the main indicators of churn. The project focuses on a four-month window, wherein the first two months are the ‘good’ phase, the third month is the ‘action’ phase, while the fourth month is the ‘churn’ phase. The business objective is to predict the churn in the last i.e. fourth month using the data from the first three months.