Handle Missing Data: fillna, dropna, interpolate

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Contents

1: Data Science Full Course For Beginners

1.1: What is Data Science?

1.2: Learn data science for beginners

1.3: Install python on windows

1.4: Variables in python

1.5: Variables in python Exercise

1.6: Numbers

1.7: Numbers Exercise

1.8: Strings

1.9: Strings Exercise

1.10: Lists

1.11: List Exercise

1.12: Install PyCharm on Windows

1.13: Debug Python code using PyCharm

1.14: If Statement

1.15: For loop

1.16: For Loop Exercise

1.17: Functions

1.18: Functions Exercise

1.19: Dictionaries and Tuples

1.20: Dictionaries and Tuples Exercise

1.21: Install Python Module (using pip)

1.22: Modules

1.23: Working With JSON

1.24: if __name__ == "__main__"

1.25: Exception Handling

1.26: Class and Objects

1.27: What is Jupyter Notebook?

1.28: What is Anaconda? Install Anaconda On Windows.

1.29: Jupyter Notebook Tutorial / Ipython Notebook Tutorial

1.30: numpy tutorial - basic array operations

2: Python Pandas Tutorial

2.1: What is Pandas python? Introduction and Installation

2.2: Dataframe Basics

2.3: Different Ways Of Creating DataFrame

2.4: Read Write Excel CSV File

2.5: Handle Missing Data: fillna, dropna, interpolate

2.6: Handle Missing Data: replace function

2.7: Group By (Split Apply Combine)

2.8: Concat Dataframes

3: Matplotlib Tutorial

3.1: Introduction and Installation

3.2: format strings in plot function

3.3: Axes labels, Legend, Grid

3.4: Bar Chart

3.5: Histograms

3.6: Pie Chart

4: Machine Learning Tutorial Python

4.1: How to become a data scientist for free? | Step by step approach to become data scientist

4.2: What is Machine Learning?

4.3: Linear Regression Single Variable

4.4: Linear Regression Single Variable Quiz

4.5: Linear Regression Single Variable Exercise

4.6: Linear Regression Multiple Variables

4.7: Linear Regression with Multiple Variables Quiz

4.8: Linear Regression Multiple Variables Exercise

4.9: Gradient Descent and Cost Function

4.10: Gradient Descent and Cost Function Quiz

4.11: Gradient Descent and Cost Function Exercise

4.12: Save Model Using Joblib And Pickle

4.13: Dummy Variables & One Hot Encoding

4.14: Dummy Variables & One Hot Encoding Quiz

4.15: Dummy Variables & One Hot Encoding Exercise

4.16: Training and Testing Data

4.17: Logistic Regression (Binary Classification)

4.18: Logistic Regression (Binary Classification) Quiz

4.19: Logistic Regression (Binary Classification) Exercise

4.20: Logistic Regression (Multiclass Classification)

4.21: Logistic Regression (Multiclass Classification) Exercise

4.22: Decision Tree

4.23: Decision Tree Exercise

4.24: Support Vector Machine (SVM)

4.25: Support Vector Machine (SVM) Exercise

4.26: Support Vector Machine Quiz

4.27: Random Forest

4.28: Random Forest Exercise

4.29: Random Forest Quiz

4.30: K Fold Cross Validation

4.31: K Fold Cross Validation Exercise

1.24: if name == "main"