To predict the score of a student based on # of hours studied Used Linear regression to unvariante regression of independent variable Hours to predict the dependable variable Scores and further used this regression model to predict the score of a student who studies for 9.25 hrs/ day.
The model validation has been evaluated with Goodness of Fitness - R2, MSE. Also tested T-test and F-test stastictics to evaluate the model.
Task : From the given 'IRIS' dataset predict the optimal number of clusters and represents it visually
Data set : https://bit.ly/3kXTdox
The task is :
(i) As a business manager, try to find out the weak areas where you can work to make more profit.
(ii) What all business problems you can derive by exploring the data?
(iii) Dash Boards - explaining the charts and interpretations.
Global Terrorism Attacks Aalyzed time interval is 1970-2017, except 1993. The data analysis based on 4,000,000 new articles and 25,000 new sources.
-This Data Analysis contains;
More than 180,000 Terrorist Attacks More than 75,000 bombings 17,000 assassinations 9,000 kidnappings
As a sports analysts, find out the most successful teams, players and factors contributing win or loss of a team. Suggest teams or players a company should endorse for its products. StoryBoards - explaining the charts and interpretations. Use annotations, animation and images.
Observations The information depicted is of 9 seasons of IPL and a clear trend can be seen for the match winning combination of team members and the batting strengths. It was seen that Mumbai Indians played the most number of matches. Virat Kohli was the best batsman and has scored against some of the best bowlers The information shown for the opponents of Delhi Daredevils would include the bowlers against whom Virat performed poorly.
The top batsmen have been consistent in their performance.
Mumbai Indians have needed more games for each win while teams like CSK have needed less to win.
For the given ‘Iris’ dataset, create the Decision Tree classifier and visualize it graphically.
● The purpose is if we feed any new data to this classifier, it would be able to predict the right class accordingly.
● Dataset : https://bit.ly/3kXTdox
To predict the class of species used the following decision tree algorithms: 1. Decision Tree Classifier - Fully Grown 2. Decision Tree Classifier - With Optimal Depth 3. Decision Tree Classifier - Grid Search
- Create a hybrid model for stock price/performance prediction using numerical analysis of historical stock prices, and sentimental analysis of news headlines In this I have predicted if a companies stock will increase or decrease based on news headlines using sentiment analysis.This model will determine if the price of a stock will increase or decrease based on the sentiment of top news article headlines for the current day using Python and machine learning.
I have used both numerical and textual data for this.
(i) Time series analysis is performed on the Stock data
(ii) Sentiment analysis is performed on the News data.
(iii) An analysis is performed by merging both the data to predict if the Close price of the stock will increase or decrease.