Module 3 - Basics of Data Manipulation in Time Series

Module 3 - Basics of Data Manipulation in Time Series

• 1.
  Kashif Murtaza AI SciencesInstructor A Practical Approach to Timeseries Forecasting using Python • Basic Data Manipulation in Time Series Shahzaib Hamid • How to Install Packages? • Basic Plotting and Data Visualization in Python • Dataset Manipulation and Slicing • Overview of Time Series Parameters
• 2.
  Section Overview? ▪ Pandas,Numpy, MatplotLib and scikit-learn are basic important libraries of python. ▪ Pandas is used to develop and modify data frames. ▪ Numpy is one of the most commonly used packages for scientific computing in Python. ▪ Matplotlib is a cross-platform, data visualization and graphical plotting library for Python. ▪ Scikit-learn (Sklearn) is the most useful and robust library for machine learning in Python.
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  Packages Installation? Recommended Tool: ▪Anaconda Distribution of Python Steps: ▪ Visit Anaconda.com/downloads ▪ Select Windows ▪ Download the .exe installer ▪ Open and run the .exe. Installer ▪ Open the Anaconda Prompt and you are good to go ▪ After Installation, create new environment on Anaconda ▪ pip install Jupyter notebook ▪ Install dependencies
• 4.
  Basic Plotting andData Visualization in Python There are mainly three core frameworks for data visualization in Python. 1. Basic matplotlib 2. Area Plots, Histograms, and Bar Plots 3. Pie Charts, Box Plots, Scatter Plots, and Bubble Plots
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  Overview of Data Visualization Basicmatplotlib Area Plots, Histograms, and Bar Plots Pie Charts, Box Plots
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  Matplotlib Install Dependencies Load Dataset Data slicing Data visualization using matplotlib
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  Area Plots, Histograms, and BarPlots Load Dataset Data Sorting Data visualization of Area Plot
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  Histogram and Pie Charts Histogram PieCharts
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  Overview of TimeSeries Parameters Correlation MAE RMSE MAPE
• 10.
  Correlation • It canbe useful in data analysis and modeling to better understand the relationships between variables. • The statistical relationship between two variables is referred to as their correlation. • numpy, matplotlib
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  Mean Absolute Error •Mean Absolute Error calculates the average difference between the calculated values and actual values. • It is also known as scale-dependent accuracy as it calculates error in observations taken on the same scale. • It is used as evaluation metrics for regression models in machine learning. • scikit-learn
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  Root Mean SquareError • Root Mean Square Error, which is the square root of value obtained from Mean Square Error function. • Using RMSE, we can easily plot a difference between the estimated and actual values of a parameter of the model. • scikit-learn
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  Mean Absolute PercentageError • It is a statistical measure to define the accuracy of a machine learning algorithm on a particular dataset. • Can be considered as a loss function to define the error termed by the model evaluation. • It helps estimate the accuracy in terms of the differences in the actual v/s estimated values. • scikit-learn