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Name: Pankush Kukreja
Type: User
Bio: A Developer exploring the field of Machine Learning and Deep Learning.
Location: California
Name: Pankush Kukreja
Type: User
Bio: A Developer exploring the field of Machine Learning and Deep Learning.
Location: California
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.
Here i am using Bank data which will predict if he customer will leave Bank or not using Artificial Neural network.
Big Mart Sales Full Intuition
Book recommendation collaborative filtering system using Pearson correlation and nearest neighbor algorithm
XGboost and Gradient Boosting implementation
Implementation of Cat Boost using Annual Income prediction data.
Here i have used mall customers data to implement KMeans, Hierarchal and DBScan clustering algorithms
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.
This is the implementation of all type of Cross validation and Back ward elimination technique
The resources of the preparation course for Databricks Data Engineer Associate certification exam
In this i explained all aspects of Exploratory data analysis using Wine Quality data set from kaggle
How we can choose our best features for a model from a dataset
building a recommendation system using graph search methodologies. We will be comparing these different approaches and closely observe the limitations of each.
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.
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.
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.
This is multinomial dataset which i used to explain all the classification algorithm with feature selection using LDA and boosting using Ada Boost
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.
In this you can see the implementation of basics preprocessing of text from paragraph, Bag of words and TF-IDF.
This is how i expertise in Pandas
Pipeline purpose
Recommendation System using ML and DL
Explaining recommendation and its type
Recommendation System implementation which includes user based collaborative filtering, item based recommender and content boosted collaborative filtering using Python.
I have 1000 reviews for a restaurant based on that i will classify them as 1(Good) and 1(bad).
Monthly sales prediction of a furniture store using past daily order data.
i will be processing 5000 sms and will classify them as Spam or not
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China tencent open source team.