Introduction
Welcome to this new blog. In this post, we’re going to discuss a new project requirement which is "Customer Data Enhancement: Preprocessing for Analysis". This Project aimed to refine a dataset containing customer information by addressing missing values, converting categorical data, normalizing variables, and introducing transformations to facilitate accurate analysis.
We'll walk you through the project requirements, highlighting the tasks at hand. Then, in the solution approach section, we'll delve into what we've accomplished, discussing the techniques applied and the steps taken. At last , In the output section, we'll showcase key screenshots of the results obtained from the project.
Let's get started!
Project Requirement :
Assignment Task
Dataset
Customer ID | Age | Income | Year-of-Education | Purchase-Amount | Favorite |
1 | 35 | 77,000 | 20 | 643 | YES |
2 | 25 | 26,000 | 11 | 343 | YES |
3 | 26 | 113,000 | 13 | 409 | YES |
4 | 23,000 | 8 | 405 | YES | |
5 | 53 | 107,000 | 586 | YES | |
6 | 25 | 31,000 | 12 | 425 | Not_Fav |
7 | 33 | 134,000 | 5 | 367 | |
8 | 60 | 44,000 | 10 | 422 | More_Than_Fav |
9 | 25 | 36,000 | 9 | 447 | More_Than_Fav |
10 | 39 | 87,000 | 11 | 532 | Not_Fav |
Imputation
Please impute the missing values for variables “Age”, “Year-of-Education” and “Favorite”
Categorical Data Conversion
After imputation, next please convert variable “Favorite” to numerical
Normalization
Next please normalize variables “Age” and “Income”
Transformation
Next please create two variables
variable “Square Root of Income”, so that for every row, the value of “Square Root of Income” is the value of square root of variable “Income”
variable “Combined Age and Income”, so that for every row, the value of “Combined Age and Income” is 0.5*Age+0.6*Income
Assignment Submission
Please submit one Word file including results.
Please submit your Python file.
Solution Approach
In this project, we tackled a data preprocessing task to enhance the usability and accuracy of our dataset. Here's a breakdown of the methods and techniques used:
Dataset: We started by using a dataset containing information about customers, including their age, income, education level, purchase amount, and their favorite status.
Data Processing Techniques: Our first step was to address missing values in our dataset. We employed the SimpleImputer class from scikit-learn to fill in missing values for the variables "Age", "Year-of-Education", and "Favorite". We used various strategies such as median imputation for age, mean imputation for year-of-education, and mode imputation for the favorite variable.
Categorical Data Conversion: We converted the categorical variable "Favorite" into numerical format using LabelEncoder from scikit-learn. This step is crucial as many machine learning algorithms require numerical inputs.
Normalization: To ensure that variables were on a similar scale, we applied normalization to the "Age" and "Income" variables. This helps prevent certain variables from dominating others in the dataset, which can skew the results of certain algorithms.
Transformation: We performed transformations on the data to derive new variables. We calculated the square root of income for each row, creating a new variable called "Square Root of Income". Additionally, we created a combined variable named "Combined Age and Income", which is a linear combination of age and income.
Output
The successful completion of the Customer Data Enhancement project underscores our commitment to delivering comprehensive solutions tailored to meet our clients' needs. Through meticulous data preprocessing techniques, we transformed raw data into a refined dataset primed for analysis, enhancing its usability and accuracy.
By addressing missing values, converting categorical data, normalizing variables, and introducing transformative elements, we've not only optimized the dataset for machine learning algorithms but also laid the groundwork for meaningful insights and informed decision-making.
At Codersarts, we recognize the pivotal role data preprocessing plays in unlocking the true potential of data-driven initiatives. Our expertise in data science and analytics empowers organizations to harness the full power of their data, driving innovation, efficiency, and ultimately, success.
As we continue to push the boundaries of possibility in data analytics, we remain steadfast in our commitment to delivering excellence, driving value, and exceeding expectations every step of the way.
If you require any assistance with the project discussed in this blog, or if you find yourself in need of similar support for other projects, please don't hesitate to reach out to us. Our team can be contacted at any time via email at contact@codersarts.com.