Repository containing all the data science project that i have worked on. These implementations are in python (jupyter notebooks )and R.
feel free to connect : here
link to my website: here
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identify the sentiments :This time around, given the tweets from customers about various tech firms who manufacture and sell mobiles, computers, laptops, etc, the task is to identify if the tweets have a negative sentiment towards such companies or products.
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swahili news classification : The objective is to develop a multi-class classification model to classify news content according to their specific categories specified.The model can be used by Swahili online news platforms to automatically group news according to their categories and help readers find the specific news they want to read. In addition, the model will contribute to a body of work ensuring that Swahili is represented in apps and other online products in the future.data set.
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ZINDI vaccinate : The objective of this challenge is to develop a machine learning model to assess if a Twitter post related to vaccinations is positive, neutral, or negative. This solution could help governments and other public health actors monitor public sentiment towards COVID-19 vaccinations and help improve public health policy, vaccine communication strategies, and vaccination programs across the world.data set.
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Computer Vision : Fatalities due to traffic delays of emergency vehicles such as ambulance & fire brigade is a huge problem. In daily life, we often see that emergency vehicles face difficulty in passing through traffic. So differentiating a vehicle into an emergency and non emergency category can be an important component in traffic monitoring as well as self drive car systems as reaching on time to their destination is critical for these services. In this problem, you will be working on classifying vehicle images as either belonging to the emergency vehicle or non-emergency vehicle category. For the same, you are provided with the train and the test dataset. Emergency vehicles usually includes police cars, ambulance and fire brigades.data set