vaibhav-sing-bisht / bank-customer-segmentation-and-insurance-claim-prediction

Course Data Mining The project involved drawing inferences from 2 case studies, namely - Bank Marketing & Insurance. The concepts of Clustering, CART, Random Forest, Artificial Neural Network are used to draw inferences from these case studies. Various performance metrics have been used to validate the performance of predictions on Test & Train sets. Skills and Tools Clustering, CART, Random Forest, Artificial Neural Networks

Hello there! My name is Vaibhav

👀 I’m interested in .....

🔡 Model building, 📈 Data science, 🔃 Machine Learning, 📊 Data Visualization, 🧑‍🔧 Mechanical Engineering 📸 photography.

👨‍🎓 I’m currently learning ...Post Graduation in Data science and Business Analytics.

👷‍♂️ Favourite Quote: Never Give Up.

🤝 I’m looking to collaborate 💞️ on projects to gain experience in Machine Learning & Data Science.

👨‍💻: Stack

🛰️ Connect with me:

[email protected]

Anyone can also contact me via
[phone]

[phone]

🔧 Job Responsiblities

Skill	Technologies worked on
Programming	Python, R , C++
DataBase	MYSQL, Sqlite
Python for Data Science	Python Functions, Data Interpretation
Tools/ IDE	PyCharm, Jupyter Notebook
Machine Learning	EDA, ML-Algorithms, Execution with python,Support Vector Machine - K Nearest Neighbour - Naive Bayes, Ensemble Techniques, Logistic Regression - Linear Discriminant Analysis
Deep Learning	Neural Networks , Computer Vision, Transfer learning, Execution with Python
Natural Language Processing	Neural Network , Transfer learning, Execution with Python ,Text Mining Analytics
Advanced Statistics	ANOVA, EDA, Descriptive Statistics, Probability, Estimation, Hypothesis Testing, PCA
Data Mining	Clustering, CART, Random Forest
Predictive Modeling	Linear Regression, Logistic Regression, Linear discriminant Analysis
Data Visualization	TABLEAU , PowerBI (Microsoft Certified (Exam DA-100)
Cloud	Basic AWS , Azure
Operating System	MacOs, Windows
Version control	GIT

📔 Skill Elaboration

Skill	Technologies used to work or Known
Python	Numpy, Pandas,Spark ,Matplotlib, Seaborn, Plotly, Scikit-Learn, pickle, Keras, Open-cv, Tensorflow, Pytorch
Machine Learning	Linear Regression, Logistic Regression, Decision Tree, Support vector machine, Naive Bayes, Ensemble technique, Hyper parameter tunning
Deep Learning	Artificial Neural Network, Convolutional Neural Network, Recurrent Neural Network
Natural Language Processing	Encoder-Decoder, Self Attention, Transformer, Transfer Learning models

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