Introduction to Data Analysis
Data analysis is a process of extracting insights from data. In this article, we will go through a step-by-step guide on how to analyze a dataset, specifically a "Rishta" dataset, which contains information about candidates with different features.
Step 1: Gathering the Dataset
First, we need to gather data about several candidates with different features like job type, salary, location, height, etc. This dataset will be used to analyze and extract insights.
Step 2: Cleaning the Data
After gathering the data, the first task is to check for any null values or duplicates. This is done to keep the data clean and organized. If there are any missing values, we can use mean/mode/median to fill the null values, or we can also drop the missing data if the null values are less.
Step 3: Converting Categorical Data
Next, we need to check the data types of each column. If the features are categorical (like Job Type), we need to convert them into their numerical form using one-hot encoding, as computers can only understand numbers. One-hot encoding creates separate columns for each category and indicates "1" for the presence of a category and "0" for the absence of a category.
How One-Hot Encoding Works
Let’s say we have a student’s table with a categorical ‘performance’ column. The ‘performance’ contains categories like excellent, good, average, and needs to improve. When we apply one-hot encoding on this column, it creates separate columns for each category. It then indicates "1" for the presence of a category and "0" for the absence of a category.
Step 4: Applying Statistical Tests
After converting all data to numerical values, we need to apply statistical tests. In this case, we used two methods: the Chi-Square test on the categorical columns and the ANOVA F-test on the numerical columns. We also calculated the "p-value" for each test.
Step 5: Selecting Features Based on P-Value
We consider the threshold value to be 0.05. The features whose p-value is less than the threshold value are selected, and the features whose p-value is greater than the threshold are rejected.
Results
The features that are selected are the ones whose p-value is less than the threshold value. These features are the most relevant and will be used for further analysis.
Conclusion
In conclusion, analyzing a dataset involves several steps, including gathering the data, cleaning the data, converting categorical data, applying statistical tests, and selecting features based on p-value. By following these steps, we can extract insights from the data and make informed decisions. The complete code for this analysis can be found on GitHub.
