Pinned Repositories
Airline-Fares-Analysis
Understanding Airline Fares with EDA, Uni-variate , Bi-variate methods and creating statistical methods.
akashjaiswal
Amazon-Recommendation-System
The objective of the Repo is to understand and build a recommendation system to recommend products to customers based on the their previous ratings for other products.
Bank-Note-Authentication
Understanding the importance of PCA on the dataset
Credit-Card-Fraud-Detection
Created this Repo to use various predictive models to see how accurate they are in detecting whether a transaction is a normal payment or a fraud. As described in the dataset, the features are scaled and the names of the features are not shown due to privacy reasons. Nevertheless, we can still analyze some important aspects of the dataset.
Depression---Clustering
To determine if there is a relationship between higher levels of black and white thinking and higher levels of self-reported depression in psychiatric patients hospitalized for depression.Also apply K means clustering and assign groups for model prediction
Fashion-MNIST---Keras
Predicting Fashion apparels such as Shoes, Shopping Bags, Trousers, etc with Keras API
Loss-Estimation
Automating loss estimation based on customer detail provided while applying for a loan.
Recommendation-Systems
Repo created to understand three very important types of recommender systems: Collaborative Filtering, Content-Based Filtering, Hybrid Recommendation Systems
Street-View-House-Numbers-recognition
The objective of the repo is to learn how to implement a simple image (SVHN Data) classification pipeline based on a deep neural network.
akashjaiswal's Repositories
akashjaiswal/Loss-Estimation
Automating loss estimation based on customer detail provided while applying for a loan.
akashjaiswal/Street-View-House-Numbers-recognition
The objective of the repo is to learn how to implement a simple image (SVHN Data) classification pipeline based on a deep neural network.
akashjaiswal/Airline-Fares-Analysis
Understanding Airline Fares with EDA, Uni-variate , Bi-variate methods and creating statistical methods.
akashjaiswal/akashjaiswal
akashjaiswal/Amazon-Recommendation-System
The objective of the Repo is to understand and build a recommendation system to recommend products to customers based on the their previous ratings for other products.
akashjaiswal/Bank-Note-Authentication
Understanding the importance of PCA on the dataset
akashjaiswal/Credit-Card-Fraud-Detection
Created this Repo to use various predictive models to see how accurate they are in detecting whether a transaction is a normal payment or a fraud. As described in the dataset, the features are scaled and the names of the features are not shown due to privacy reasons. Nevertheless, we can still analyze some important aspects of the dataset.
akashjaiswal/Depression---Clustering
To determine if there is a relationship between higher levels of black and white thinking and higher levels of self-reported depression in psychiatric patients hospitalized for depression.Also apply K means clustering and assign groups for model prediction
akashjaiswal/Fashion-MNIST---Keras
Predicting Fashion apparels such as Shoes, Shopping Bags, Trousers, etc with Keras API
akashjaiswal/Recommendation-Systems
Repo created to understand three very important types of recommender systems: Collaborative Filtering, Content-Based Filtering, Hybrid Recommendation Systems
akashjaiswal/Boston-House-Price
akashjaiswal/Boston-House-Price-Prediction
The most basic dataset available to practice the concepts of regression analysis.
akashjaiswal/Breast-Cancer-Diagnosis
This is a repo to classify the Benign and Malignant cells in the given data set using the description about the cells in the form of columnar attributes. There are Visualizations and Analysis for Support.
akashjaiswal/Breast-Cancer-Type-Detection
Using KNN to predict the type of Breast Cancer in the Breast Cancer Wisconsin(Diagnostic)Data.
akashjaiswal/Car-Clustering
Repo created to understand and use k-means clustering for grouping the similar cars in one cluster.
akashjaiswal/Car-Mileage-Prediction
Constructing a linear model that explains the relationship a car's mileage (mpg) has with its other attributes. Also learn more about Regularization techniques such as Ridge and Lasso.
akashjaiswal/Census-Income-prediction
To predict whether income exceeds 50K/yr based on census data
akashjaiswal/Cervical-Cancer-Prediction
Created this Repo to predict the patients who are most likely to suffer from cervical cancer using Machine Learning algorithms for Classification, Visualizations, and Analysis.
akashjaiswal/Concrete-Strength-Prediction
The Repo is created to understand modeling of strength of high performance concrete using Machine Learning.
akashjaiswal/Credit-Risk-Modelling
The Repo contains data driven risk models which calculates the chances of a borrower defaults on loan or credit card calculated on basis of n factors
akashjaiswal/Customer-Hierarchial-Clustering
Clustering customers based on their Spending strategy
akashjaiswal/Customer-Profiling
This Repo is created for understanding descriptive analytics to create a customer profile for each CardioGood Fitness treadmill product line
akashjaiswal/Graduate-Admission-Prediction
The Repo is created to predict the chances of getting admitted for Masters program considering dimensions such as GRE Scores , TOEFL Scores, etc
akashjaiswal/Network-Intrusion-Detection
The repo is created to understand KNN by detecting network intrusion by learning from 3 qualitative and 38 quantitative features.
akashjaiswal/Pima-Indians-Diabetes
The purpose is to predict whether the Pima Indian women shows signs of diabetes or not. We are using a dataset collected by "National Institute of Diabetes and Digestive and Kidney Diseases" which consists of a number of attributes which would help us to perform this prediction.
akashjaiswal/Technical-Support-Data-Analysis
Performing K-Means
akashjaiswal/test-repo
this is jst a test repo
akashjaiswal/Vehicle-Classification
The purpose of the case study is to classify a given silhouette as one of three different types of vehicle, using a set of features extracted from the silhouette. One of the purposes of this case study is to understand any dimensionality curse using PCA.
akashjaiswal/Vehicle-Clustering-Using-HC
The data set has information about features of silhouette extracted from the images of different cars Four "Corgie" model vehicles were used for the experiment: a double decker bus, Cheverolet van, Saab 9000 and an Opel Manta 400 cars. This particular combination of vehicles was chosen with the expectation that the bus, van and either one of the cars would be readily distinguishable, but it would be more difficult to distinguish between the cars.