Hello!

This is a Exploratory Data Analysis on the most popular franchise cricket league in the entire cricketing world. We collect data and analyse all matches played between 2008 and 2019 and bring out inferences and conclusions. We gather multiple information like winning probability on the basis of toss, most successful team, etc.

This project is a supervised machine learning algortihm which predicts the scores of the students based on their hours of study. This requires Python and Scikit to implement regression. We also use linear regression to predict the changes in the value of our data. In this regression task we predict the percentage of marks that a student is expected to score based upon the number of hours they studied. This is a simple linear regression task as it involves just two variables.

This analysis tells us about number of immigrants who have migrated to Canada over the past 30 years. The number of immigrants moving to Canada depends upon various factors like Education and Job opportunities. So we compare countries on the basis of how many immigrants have migrated in any particular year for example in 2001. We can also find out between multiple countries how many immigrants have been migrated over there. We can also modify our choices by providing inputs based on different criterias like from which continents the immigrants were most or least. We then plot the graph for any random country and plot for all the years that they have provided immigrants to move to Canada. Even a line graph has been plotted in order to check for all or many countries how many immigrants have they sent over a period of time or a particular time. We can also create a top trending graph with the major lead in the countries in sending immigrants by plotting multiple line graph..

From the IRIS dataset we predict the optimum number of clusters and present it visually. Python language is used to import scikit,pandas,numpy and matplotlib module in order to implement the model. The optimum number of clusters followed by the value of K for which the clustering occurs.

This is a prediction based model on Decision Tree Classifier and visualise data graphically. The purpose is we feed any data into this classifier and we can predict the right class accordingly.

There is comparison of 5 subjects which are very common amongst students in San Francisco,CA. These include Data Analytics, Deep Learning, Big Data using Spark or Hadoop, Data Visualization and Data Journalism. Plot the graph between people who are very interested ,somewhat interested and those who are not at all interested.

This project briefly discusses about the different prices about the wheel on the basis of car type not the model. Import various libraries like pandas and numpy and graphs like seaborn and matplotlib and present data, classify accordingly to provide different attributes to relevant groups. Plot the graph using seaborn between engine-size, highway-mileage and price; classify, group and plot peak-rpm and price with table alongwith stroke-price variations. Make boxplot for all the attributes and graph accordingly. Filter and furnish data on the basis of wheel-drive like AWD,4WD,FWD,RWD. Group following cars using engine location attribute and detect the price to get a solid output like table data based on wheel drive, car type and the price. Import matplotlib in the picture and group colours for different types of attributes and prices. and import stats from scipy and find Pearson Correlation Coefficient and ANOVA results which states the changes in our car price with the given attriutes like Body type, rpm, etc.