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bajaj-stocks-predictions-using-arima

Calculating the stocks of bajaj using ARIMA model

cifar10

The CIFAR-10 dataset consists of 60000 32x32 colour images in 10 classes, with 6000 images per class. There are 50000 training images and 10000 test images. For Cifar10 database - You are given: 1. Readme file has Template file to download cifar10 dataset (it has training and test dataset). You have to download dataset using this. Your task is to: 1. Predict correct class for every image in the test dataset.

clicast

Broadcast messages for CLI tools, such as a warning for critical bug or notification about new features.

dsa

Building the largest DSA solutions repository TOGETHER.

gradient-descent---combined-cycle-power-plant

A Model to get prediction of combined-cycle dataset

image-search

indian-startup

This dataset has funding information of the Indian startups from January 2015 to August 2017.

indian-startup2

Your Friend has developed the Product and he wants to establish the product startup and he is searching for a perfect location where getting the investment has a high chance. But due to its financial restriction, he can choose only between three locations - Bangalore, Mumbai, and NCR. As a friend, you want to help your friend deciding the location. NCR include Gurgaon, Noida and New Delhi. Find the location where the most number of funding is done. That means, find the location where startups has received funding maximum number of times. Plot the bar graph between location and number of funding. Take city name "Delhi" as "New Delhi". Check the case-sensitiveness of cities also. That means, at some place instead of "Bangalore", "bangalore" is given. Take city name as "Bangalore". For few startups multiple locations are given, one Indian and one Foreign. Consider the startup if any one of the city lies in given locations. Even after trying for so many times, your friend’s startup could not find the investment. So you decided to take this matter in your hand and try to find the list of investors who probably can invest in your friend’s startup. Your list will increase the chance of your friend startup getting some initial investment by contacting these investors. Find the top 5 investors who have invested maximum number of times (consider repeat investments in one company also). In a startup, multiple investors might have invested. So consider each investor for that startup. Ignore undisclosed investors. After re-analysing the dataset you found out that some investors have invested in the same startup at different number of funding rounds. So before finalising the previous list, you want to improvise it by finding the top 5 investors who have invested in different number of startups. This list will be more helpful than your previous list in finding the investment for your friend startup. Find the top 5 investors who have invested maximum number of times in different companies. That means, if one investor has invested multiple times in one startup, count one for that company. There are many errors in startup names. Ignore correcting all, just handle the important ones - Ola, Flipkart, Oyo and Paytm. Even after putting so much effort in finding the probable investors, it didn't turn out to be helpful for your friend. So you went to your investor friend to understand the situation better and your investor friend explained to you about the different Investment Types and their features. This new information will be helpful in finding the right investor. Since your friend startup is at an early stage startup, the best-suited investment type would be - Seed Funding and Crowdfunding. Find the top 5 investors who have invested in a different number of startups and their investment type is Crowdfunding or Seed Funding. Correct spelling of investment types are - "Private Equity", "Seed Funding", "Debt Funding", and "Crowd Funding". Keep an eye for any spelling mistake. You can find this by printing unique values from this column. There are many errors in startup names. Ignore correcting all, just handle the important ones - Ola, Flipkart, Oyo and Paytm. Due to your immense help, your friend startup successfully got seed funding and it is on the operational mode. Now your friend wants to expand his startup and he is looking for new investors for his startup. Now you again come as a saviour to help your friend and want to create a list of probable new new investors. Before moving forward you remember your investor friend advice that finding the investors by analysing the investment type. Since your friend startup is not in early phase it is in growth stage so the best-suited investment type is Private Equity. Find the top 5 investors who have invested in a different number of startups and their investment type is Private Equity. Correct spelling of investment types are - "Private Equity", "Seed Funding", "Debt Funding", and "Crowd Funding". Keep an eye for any spelling mistake. You can find this by printing unique values from this column.There are many errors in startup names. Ignore correcting all, just handle the important ones - Ola, Flipkart, Oyo and Paytm.

insta-bot

Your friend has opened a new Food Blogging handle on Instagram and wants to get famous. He wants to follow a lot of people so that he can get noticed quickly but it is a tedious task so he asks you to help him. As you have just learned automation using Selenium, you decided to help him by creating an Instagram Bot. You need to create different functions for each task.

linear-regression---diabetes-dataset

Diabetes dataset is one of the datasets available in sklearn. The diabetes dataset consists of 10 physiological variables (age, sex, weight, blood pressure) measure on 442 patients, and an indication of disease progression after one year. You are given a Training dataset csv file with X train and Y train data. As studied in lecture, your task is to come up with Linear Regression training algorithm and thus predictions for the test dataset given.

logistic-regression---titanic-dataset

myphotoapp

push-notification-using-react-native

Simple android and ios application for push notifications using firebase and react-native

python-problems

365 days coding challenge

resume

self-driven-car

spinny-assignment

sturdy-barnacle

text-classification-

. Perform Test Classification using Multinomial Naive Bayes(already implemented in sklearn). 2. Implement Naive Bayes on your own from scratch for text classification. 3. Compare Results of your implementation of Naive Bayes with one in Sklearn.

twitter-us-airline-sentiment-analysis

Twitter US Airline Sentiment Analysis Send Feedback Given Twitter US Airline Sentiment Dataset, which contains data for over 14000 tweets, your task is to predict the sentiment of the tweet i.e. positive, negative or neutral. You are given: 1. A Training dataset csv file with X train and Y train data 2. A X test File and you have to predict and submit predictions for this file.

zomato-api-project

This project about Zomato's outlets, locations, cuisines of related restaurants