raobabar/10_Different_Datasets_For_Practice_ML

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raobabar/10_Different_Datasets_For_Practice_ML

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Here are 10 different datasets available globally for the practice of data analysis and machine learning.

Detection-of-Heart-disease-with-CNN

Flask app for detection of Heart Disease through CNN. In this app, patients can create their account and can predict their heart disease, and can contact with related doctors through messaging. Doctors can also create their account and check their patients and contact with them through messaging. An admin account is also included in this flask app to maintain the data, update, delete or add data and assign doctors to the patients etc. (Note: It is in it's initial stage. So, you can consider it version~0.1) Enjoy :)

Methods_For_Dimensionality_Reduction

Conversion-from-Text-to-speech

Heart-disease-prediction-through-different-ML-algorithms

This is a simple Heart disease prediction with 6 different algorithms. The main aim of this is to analyze that which algorithm is best and suitable for this dataset to predict.

RegularizationOfLinearModels

Regularization of Linear Models with SKLearn

Jupyter Notebook

Simple-Notepad-in--Java

I just created this simple notepad in Java to learn, explore and implement the Java programming. :)

Metro-bus-Ticket-Reservation-System

Conversion-from-Video-to-Image

Jupyter Notebook

Data-Scraping-Projects

These Data Scraping projects are used for educational purpose only, not for commercial use.

E-commerce_web

Created an E-commerce web as a semester project of "web engineering" in my 6th semester. :)

ODI_CRICKET_MATCH_PREDICTION

ODI Cricket Match prediction

Jupyter Notebook

Data_Normalization-Standardization

Data Normalization Normalization refers to rescaling real valued numeric attributes into the range 0 and 1. Data Standardization Standardization refers to shifting the distribution of each attribute to have a mean of zero and a standard deviation of one (unit variance).

Jupyter Notebook

Purchase_data_to_find_hidden_patterns

This is the dataset from a small online store (E-Commerce products) with customer purchase data in November 2018 for the analysis and to find hidden patterns and specific user behaviors within this dataset. The data include columns: [date, customer_id, product_category, payment_method, value, time_on_site, clicks_in_site] *Notes: * ● Product_category is an id for a category of products (for example clothing, gadgets…). ● Each row is a cart (1-many items) for a customer. assume customers purchase from a single category each time. ● Payment_method can be credit card or Paypal ● Value is the total value for the cart (can include any number of items) time_on_site is in minutes

How_to_reduce_memory_size_of_large_datasets