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Robotic Future, ML Model, Confusion Matrix, Find out the best ML model…

- - - - Sentiments Analysis
      - Topic Classification
      - Language Detection
- - - - Power BI Data Stories
      - Gapminder
      - Novypro
      - Netlogo
- - - - Step 3: Data Exploration - I
        
        Step 4: Data Preprocessing
        Step 4.n Feature Engineering
        Step 4.m: Data Clean ?
        Hints: Blank/errors/scale mismatch/anomalies
        
        Data Exploration - II
        
        Data Analysis
        
        Visualisation & Data Inference
        Module: Matplotlib & Seaborn & Plotly
        
        Univariate
        
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        Bivariate /Multivariate
        
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        AI Visuals
        
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        Apply ML ? Yes
        
        Split the Data 70/30
        Module: Sklearn
        
        Train
        
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        Test
        
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- - - - 4️⃣ Data Exploration - II
        df.groupby
        pd.crosstab
        
        5️⃣Data Visualisation
        Library: Matplotlib/Seaborn
        plt.hist
        plt.pie
        
        6️⃣Data Split
        from sklearn.model_selection import train_test_split
        X_train, X_test, y_train, y_test = train_test_split(X,y, randomstate=0)
        
        7️⃣ Model Selection
        from sklearn.linear_model import LinearRegression
        lr=LinearRegression(normalize=True)
        
        8️⃣Model fit
        lr.fit(X,y)
        
        9️⃣ Model Prediction
        y_pred = lr.predict(X_test)
        
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