Project 1

Objective: In this project we are going to clean the dataset of '1985 Automobile Dataset'.

About the Dataset:

This is a raw dataset about cars in 1985.

This data set consists of three types of entities:
(a) the specification of an auto in terms of various characteristics,
(b) its assigned insurance risk rating,
(c) its normalized losses in use as compared to other cars. The second rating corresponds to the degree to which the auto is more risky than its price indicates. Cars are initially assigned a risk factor symbol associated with its price. Then, if it is more risky (or less), this symbol is adjusted by moving it up (or down) the scale. Actuarians call this process "symboling". A value of +3 indicates that the auto is risky, -3 that it is probably pretty safe. The third factor is the relative average loss payment per insured vehicle year. This value is normalized for all autos within a particular size classification (two-door small, station wagons, sports/specialty, etc…), and represents the average loss per car per year.

Note: Several of the attributes in the database could be used as a "class" attribute.

Number of Instances: 205

Number of Attributes: 26 total
-- 15 continuous
-- 1 integer
-- 10 nominal.

Overview:

• Importing the necessary libraries and the raw data.
• Renaming the columns.
• Finding the missing values.
• Filling the missing values.
• Exporting the data into a new csv file.
• Summary and Conclusion.

• Summary and Conclusion:

  • The raw data was imported.
  • All the columns was renamed.
  • The null values was counted.
  • All the null values was replaced by the columns's mean value.
  • In the 'No. of Doors' column the null values are replaced by the most numbered categorical indicator.
  • The new dataframe is exported to a csv file.
  • Hence the '1985 Automobile Dataset' is cleaned.