bharathkumar-ms Goto Github PK

followers: 0.0 following: 9.0 repos: 64.0 gists: 0.0

Name: Bharathkumar M S

Type: User

Company: Rubixe

Bio: I'm a Data Science Enthusiast

Location: Bangalore

Blog: https://www.linkedin.com/in/bharathkumar-m-s

💫 About Me:

👋 Hi, I’m Bharathkumar M S
👀 I'm a Data Science Enthusiast!.
🌱 I’m currently working as Data Science Intern.
📫 How to reach me? You can contact me on any of the socials listed below.

🌐 Socials:

👀 Profile Views

💻 Tech Stack:

📊 GitHub Stats:

My Skills

Python
SQL
Statistics
Machine learning
Deep Learning
Computer Vision
NLP
Power BI

Projects

1.Flight fare prediction:[link]

2.Car price prediction:[link]

3.Plant Disease Classification:[link]

4.SMS spam classifier:[link]

5.Flipkart review :[link]

Bharathkumar M S's Projects

amazing-feature-engineering

Feature engineering is the process of using domain knowledge to extract features from raw data via data mining techniques. These features can be used to improve the performance of machine learning algorithms. Feature engineering can be considered as applied machine learning itself.

aps-fault-detection

bharathkumar-ms

Config files for my GitHub profile.

bharathkumar-ms.github.io

books

data science books

car-price-prediction-5

car_price_prediction

Car_Price_Prediction

car_price_prediction-streamlit

complete-python-3-bootcamp

Course Files for Complete Python 3 Bootcamp Course on Udemy

daily-dose-of-data-science

A collection of code snippets from the publication Daily Dose of Data Science on Substack: http://www.dailydoseofds.com/

data-science-hacks

Data Science Hacks consists of tips, tricks to help you become a better data scientist. Data science hacks are for all - beginner to advanced. Data science hacks consist of python, jupyter notebook, pandas hacks and so on.

data-science-interviews

Data science interview questions and answers

data-science-ipython-notebooks

Recently updated with 50 new notebooks! Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.