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autoencoder

cnn_pooling

deeplearning.ai-summary

This repository contains my personal notes and summaries on DeepLearning.ai specialization courses. I've enjoyed every little bit of the course hope you enjoy my notes too.

household-education-expenditure

loan-prediction-analytics-vidya

This is a famous problem of Loan Prediction by Analytics Vidya

practice-problem-big-mart-sales-iii

The data scientists at BigMart have collected 2013 sales data for 1559 products across 10 stores in different cities. Also, certain attributes of each product and store have been defined. The aim is to build a predictive model and find out the sales of each product at a particular store. Using this model, BigMart will try to understand the properties of products and stores which play a key role in increasing sales.

practice-problem-black-friday-by-av

predicting-the-costs-of-used-cars---hackathon-by-imarticus-learning

Predict the price of a used car based on a given set of features discussed below. Size of training set: 6,019 records Size of test set: 1,234 records FEATURES: Name: The brand and model of the car. Location: The location in which the car is being sold or is available for purchase. Year: The year or edition of the model. Kilometers_Driven: The total kilometres driven in the car by the previous owner(s) in KM. Fuel_Type: The type of fuel used by the car. Transmission: The type of transmission used by the car. Owner_Type: Whether the ownership is Firsthand, Second hand or other. Mileage: The standard mileage offered by the car company in kmpl or km/kg Engine: The displacement volume of the engine in cc. Power: The maximum power of the engine in bhp. Seats: The number of seats in the car. New_Price: The price of a new car of the same model. Price: The price of the used car in INR Lakhs.

random-

Random Practice Files

twitter-sentiment-analysis

Analytics Vidhya Practice Problem

twitter-sentiment-analysis---analytics-vidhya

Problem Statement The objective of this task is to detect hate speech in tweets. For the sake of simplicity, we say a tweet contains hate speech if it has a racist or sexist sentiment associated with it. So, the task is to classify racist or sexist tweets from other tweets. Formally, given a training sample of tweets and labels, where label '1' denotes the tweet is racist/sexist and label '0' denotes the tweet is not racist/sexist, your objective is to predict the labels on the test dataset. Motivation Hate speech is an unfortunately common occurrence on the Internet. Often social media sites like Facebook and Twitter face the problem of identifying and censoring problematic posts while weighing the right to freedom of speech. The importance of detecting and moderating hate speech is evident from the strong connection between hate speech and actual hate crimes. Early identification of users promoting hate speech could enable outreach programs that attempt to prevent an escalation from speech to action. Sites such as Twitter and Facebook have been seeking to actively combat hate speech. In spite of these reasons, NLP research on hate speech has been very limited, primarily due to the lack of a general definition of hate speech, an analysis of its demographic influences, and an investigation of the most effective features. Data Our overall collection of tweets was split in the ratio of 65:35 into training and testing data. Out of the testing data, 30% is public and the rest is private. Data Files train.csv - For training the models, we provide a labelled dataset of 31,962 tweets. The dataset is provided in the form of a csv file with each line storing a tweet id, its label and the tweet. There is 1 test file (public) test_tweets.csv - The test data file contains only tweet ids and the tweet text with each tweet in a new line.

whatsapp_chat_visualization

Whats app Chat Visualization