Cost Estimation

The objective of this kernel is to create a Regression model which restimates the cost of sale per unit based on number of units sold.

Our regression analysis kernel consists of 7 steps:

Step 0 - Import the Libraries:

in addition to the scikit-learn library whhich we use to perform the Regression analysis, we also use several other packages and modules:

• Pandas:used for data structures and operations for manipulating numerical tables
• Numpy: used for numerical analysis
• matplotlib.pyplot: used for plotting data
• seaborn: used for data visualization (used on top of matplotlib library)
• sklearn: used in our kernel to split the set into a training and a testing set, and to create and to evaluate the model.

Step 1 - Import the Dataset:

We import the data from a csv file, and we load it into a pandas DataFrame object.

Step 2 - Visualize Data:

We explore the dataset using a jointplots and an lmplot.

Step 3 - Train model:

We use the PolynomialFeatures module to transform our data into one with multiple columns, then we create a linear regression

Step 4 - Evaluate Model:

We plot the results using a scatter plot and we plot our polynomial plot on the graph.