Simon Waweru's repositories
Advert-success-analysis
A Kenyan entrepreneur has created an online cryptography course and would want to advertise it on her blog. She currently targets audiences originating from various countries. In the past, she ran ads to advertise a related course on the same blog and collected data in the process. She would now like to identify which individuals are most likely to click on her ads.
House-prediction-using-R
This is a project on prediction of house prices in Seattle. The aim is to find features that contribute most to house prices and build a model for the prediction of house prices.
House-price-prediction
Creating Regression model using different techniques that will predict the price of house given the number of bedrooms,size of living area,size of basement,number of floors,year it was built,year it was renovated,the location, availability waterfront and view,the grading of the house the size above and the condition of the house. I will create the model using Multiple linear regression,Quantile regression,Lasso regression,ridge regression and Elastic net. Then I will choose the best model.
Market-basket-analysis
EDA and Market basket analysis of Supermarket data, to uncover the most purchased items in home care department and the items that are likely to be purchased together
Naive-Bayes-KNN-classification
Creating prediction classification models using KNN to predict if a passenger with survive the titanic or not and using Naive Bayes to predict if an email is a spam or not
Sendy-Logistic-Challenge
Our main objective is to build a model that predict the estimated time of delivery of orders, from the point of driver pickup to the point of arrival at final destination. The solution will help Sendy enhance customer communication and improve the reliability of its service; which will ultimately improve customer experience. In addition, the solution will enable Sendy to realise cost savings, and ultimately reduce the cost of doing business, through improved resource management and planning for order scheduling.
Twitter-sentiment-analysis
Predicting what type of sentiments will be expressed depending on the type of tweet written and the location of the account. Find the best model to best predict the sentiments expressed over.Social media has become a huge part of our life. It connects people to the outer world. Social media provides a way to showcase our lives, discretely, conveniently, and on our own terms. People rely more on the posts and tweets shared on social networking sites like Twitter®, Facebook®, and Instagram®. It is anticipated that social media should guide people in getting correct and authentic information on Corona cases. There are various classification models used in machine learning. Depending on the features, accuracy, and MSE, a good model should be chosen, so it is easier to predict the sentiments that will be expressed before the tweet is written and posted
Association-rule-Mining
Association rule Mining is mining association patterns in a large data set using the Apriori Algorithm.
AUTOLIB-DATA-ANALYSIS
Analyzing shared electric car company to evaluate its usage in the city of France
Financial-inclusion-prediction
Financial Inclusion remains one of the main obstacles to economic and human development in Africa. For example, across Kenya, Rwanda, Tanzania, and Uganda only 9.1 million adults (or 13.9% of the adult population) have access to or use a commercial bank account. Traditionally, access to bank accounts has been regarded as an indicator of financial inclusion. Despite the proliferation of mobile money in Africa, and the growth of innovative fintech solutions, banks still play a pivotal role in facilitating access to financial services. Access to bank accounts enable households to save and facilitate payments while also helping businesses build up their credit-worthiness and improve their access to other finance services. Therefore, access to bank accounts is an essential contributor to long-term economic growth.
FINANCIAL-SERVICES-ANALYSIS-IN-EAST-AFRICA
Financial Inclusion remains one of the main obstacles to economic and human development in Africa. For example, across Kenya, Rwanda, Tanzania, and Uganda only 9.1 million adults (or 13.9% of the adult population) have access to or use a commercial bank account. Traditionally, access to bank accounts has been regarded as an indicator of financial inclusion. Despite the proliferation of mobile money in Africa and the growth of innovative fintech solutions, banks still play a pivotal role in facilitating access to financial services. Access to bank accounts enables households to save and facilitate payments while also helping businesses build up their credit-worthiness and improve their access to other financial services. Therefore, access to bank accounts is an essential contributor to long-term economic growth. The research problem is to figure out how we can predict which individuals are most likely to have or use a bank account. My solution will help provide an indication of the state of financial inclusion in Kenya, Rwanda, Tanzania, and Uganda, while providing insights into some of the key demographic factors that might drive individuals’ financial outcomes.
Global-suicide-rates
Have an overview of suicides rate in the world between 1995 and 2015
Kenya-GDP-between-2000-2011
An overview of Kenyan GDP for a period of 10 years. The sectors that contributed highest each year.
MachineLearning
Machine learning for beginner(Data Science enthusiast)
ms-learn-ml-crash-course-python
Code samples for the ML Crash Course learning path.
predictive-analytics-using-MLR.
A Chinese automobile company Geely Auto aspires to enter the US market by setting up their manufacturing unit there and producing cars locally to give competition to their US and European counterparts. They have contracted an automobile consulting company to understand the factors on which the pricing of cars depends. Specifically, they want to understand the factors affecting the pricing of cars in the American market, since those may be very different from the Chinese market Business Goal We are required to model the price of cars with the available independent variables. It will be used by the management to understand how exactly the prices vary with the independent variables. They can accordingly manipulate the design of the cars, the business strategy etc. to meet certain price levels. Further, the model will be a good way for management to understand the pricing dynamics of a new market
Retail-sales-analysis
An EDA on retail sales data that was provided by a chain of supermarket.
Statistical-analysis-of-the-Blue-cars-in-Auto-lib-rental-company
Out of the three cars, we want to investigate; the blue cars. We want to study if the blue cars taken during the weekends in the postal code 93500 have a significant difference to the cars taken during the weekend in the postal address 94410. Our hypothesis is : H0: mean of blue cars taken in 93500 > mean of blue cars taken in 94410 H1: mean of blues cars taken in 93500 is not greater than the blue cars taken in 94410