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Showing posts with the label Usage based insurance

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Top Questions People Ask About Pandas, NumPy, Matplotlib & Scikit-learn — Answered!

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 Whether you're a beginner or brushing up on your skills, these are the real-world questions Python learners ask most about key libraries in data science. Let’s dive in! 🐍 🐼 Pandas: Data Manipulation Made Easy 1. How do I handle missing data in a DataFrame? df.fillna( 0 ) # Replace NaNs with 0 df.dropna() # Remove rows with NaNs df.isna(). sum () # Count missing values per column 2. How can I merge or join two DataFrames? pd.merge(df1, df2, on= 'id' , how= 'inner' ) # inner, left, right, outer 3. What is the difference between loc[] and iloc[] ? loc[] uses labels (e.g., column names) iloc[] uses integer positions df.loc[ 0 , 'name' ] # label-based df.iloc[ 0 , 1 ] # index-based 4. How do I group data and perform aggregation? df.groupby( 'category' )[ 'sales' ]. sum () 5. How can I convert a column to datetime format? df[ 'date' ] = pd.to_datetime(df[ 'date' ]) ...

Tech Mahindra Launched Big data platform

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Tech Mahindra has announced the launch of Usage Based Insurance (UBI), a big data and predictive analysis driven platform for auto insurers. It allows them to adjust premiums according to actual usage patterns of individual drivers instead of industry averages. UBI Platform The platform is flexible to fit to the growing needs of global insurance enterprises, where the market is expected to grow $100 billion plus by the end of the decade. The solution utilizes the capabilities of Tech Mahindra and AT and T in the areas of multi-vertical, engineering and infrastructure capabilities. As a result of this, the global insurers can reduce their claim costs by up to 20%,while reducing eligible insured driver premiums up to 40%. This Usage Based Insurance solution is hosted on the seamless AT&T cloud and served through AT and T's global Machine-to-Machine (M2M) capabilities. What is usage based insurance This technology is already being used successfully for both car a...