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With the explosion of data in recent years, it has become essential for data scientists and Machine Learning practitioners to understand and effectively apply preprocessing techniques. Raw data often contains inconsistencies, missing values, and irrelevant features that can adversely affect the performance of Machine Learning models.
It’s crucial to grasp these concepts, considering the exponential growth of the global Data Science Platform Market, which is expected to reach 26,905.36 Similarly, the Data and Analytics market is set to grow at a CAGR of 12.85% , reaching 15,313.99 billion INR by 2027. billion INR by 2026, with a CAGR of 27.7%.
In this blog, we’ll be using Python to perform exploratorydataanalysis (EDA) on a Netflix dataset that we’ve found on Kaggle. We’ll be using various Python libraries, including Pandas, Matplotlib, Seaborn, and Plotly, to visualize and analyze the data. The type column tells us if it is a TV show or a movie. df.isnull().sum()
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