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annual-income-prediction

This is a prediction model to predict annual income for an adult whether it will be more or less than 50K. and explaining all the classification model evaluation metrics and curve like ROC, AUC, Precision _recall, Gini, Ks_score, Logloss, Concordance.

basic-ann

Here i am using Bank data which will predict if he customer will leave Bank or not using Artificial Neural network.

big-mart-prediction-model

Big Mart Sales Full Intuition

book-recommendation-system

Book recommendation collaborative filtering system using Pearson correlation and nearest neighbor algorithm

boosting-algorithms

XGboost and Gradient Boosting implementation

catboost-implementation

Implementation of Cat Boost using Annual Income prediction data.

clustering-algorithms

Here i have used mall customers data to implement KMeans, Hierarchal and DBScan clustering algorithms

credit-card-deault-assesment

This is data for customers using Credit Card , having 25 columns with their Eductaion, Martial Status, Age , Pays and previous Bill and payments for cc, so based on this information we will predict whether in future the customer will default the CC payment or not. I am using XGBoost for model, Randomised search for parameter tuning and also made it in 3 hidden layer Artificial neural network with SGD optimizer.

cross-validation-and-its-type

This is the implementation of all type of Cross validation and Back ward elimination technique

databricks-certified-data-engineer-associate

The resources of the preparation course for Databricks Data Engineer Associate certification exam

django-crud-app

eda-using-wine-quality-data-set

In this i explained all aspects of Exploratory data analysis using Wine Quality data set from kaggle

feature-selection-explanation

How we can choose our best features for a model from a dataset

graph-based-recommendation-system

building a recommendation system using graph search methodologies. We will be comparing these different approaches and closely observe the limitations of each.

house-prices-advanced-regression-techniques

This is Kaggle challenge for house prediction, it has alot of missing values which needs to be cleaned. i have used Regression ,Boosting, and Deep Neural network and tuning them.

human-activity-classification

The project involves training a Machine Learning model to classify the kind of activity a person is performing including sitting, standing, laying, walking, walking upstairs and walking downstairs using data collected from smartphones.

image-classification-using-cnn

This is the Kaggle dataset for Image classification of Dog and Cat. i have considered 5000 images out of 25000 image. Data is manually been divided as 4000 image of each class in training and 1000 image as testing.

iris-data-classification-model

This is multinomial dataset which i used to explain all the classification algorithm with feature selection using LDA and boosting using Ada Boost

kaggle-credit-card-fraud-detection

This is an highly imbalanced data with only 1.72% minority and 98.28% majority class, i will be explaining Up and down sampling and effect of sampling before and while doing cross validation. Model has been evaluated using precision recall curve.

lending-clud-data-analysis-using-pyspark

natural-language-processing-nlp-

In this you can see the implementation of basics preprocessing of text from paragraph, Bag of words and TF-IDF.

pandas-tutorials

This is how i expertise in Pandas

pipelinerepo

Pipeline purpose

react_tutorial

recommendation

Recommendation System using ML and DL

recommendation-system

Explaining recommendation and its type

recommendationsystem

Recommendation System implementation which includes user based collaborative filtering, item based recommender and content boosted collaborative filtering using Python.

restro_review_analysis-using-nlp

I have 1000 reviews for a restaurant based on that i will classify them as 1(Good) and 1(bad).

sales-prediction

Monthly sales prediction of a furniture store using past daily order data.

sms-spam-classification

i will be processing 5000 sms and will classify them as Spam or not