## Data source:https://archive.ics.uci.edu/ml/datasets/Automobile

#Data-source:

This is the automobile data with missing values. Goal is to clean the data and use machine learning algorithms to predict the price.

We are reading a csv file and appending the data in a list

We created a numpy array named as alldatanp. This will print the number of missing rows in the whole data set.

Here we are counting the total number of missing cells in this data set.

## Printing the row number with the count of values missing in that row. We are making a dictionary here and printing Key as a row number and value as the number of values missing in that row.

#Printing-the-row-number-with-the-count-of-values-missing-in-that-row.-We-are-making-a-dictionary-here-and-printing-Key-as-a-row-number-and-value-as-the-number-of-values-missing-in-that-row.

## What are the column numbers having missing values in it.

#What-are-the-column-numbers-having-missing-values-in-it.

## Printing the column number with the count of missing values in it. for example column number 1 has 41 missing values.

#Printing-the-column-number-with-the-count-of-missing-values-in-it.-for-example-column-number-1-has-41-missing-values.

## As we have 12 rows with missing values. Total number of rows are 205, around 5% rows are missing so we will not append these 12 rows in our final data set.

#As-we-have-12-rows-with-missing-values.-Total-number-of-rows-are-205,-around-5%-rows-are-missing-so-we-will-not-append-these-12-rows-in-our-final-data-set.

## Replacing non numerical variables (attributes)

#Replacing-non-numerical-variables-(attributes)