Data Science Problem Solving: A Deep Dive into Essential Steps

Data science is a rapidly growing field that has revolutionized the way organizations approach problem-solving. The ability to extract insights and knowledge from large datasets has opened up a wide range of possibilities across industries, from healthcare and finance to marketing and cybersecurity.

Check the precious post: The Essential Steps to Approach a Data Science Problem: From Problem Formulation to Result Validation

However, approaching a data science problem can be a complex process that requires careful consideration of several essential steps. In this blog, we will take a deep dive into three of the essential steps involved in data science problem-solving — Real-life Examples. Whether you’re a seasoned data scientist or just starting in the field, understanding these steps can help you approach data science problems effectively and efficiently.

Step 1: Formulate a Problem

General Question:

“How much Revenue will a move make?”

What does the revenue of a movie depend on? Formulating a hypothesis is next.

Hypothesis: The revenue generated by a movie can be predicted using various factors such as the movie genre, cast, budget, release date, and marketing campaign. By analyzing these factors and their impact on past movie revenues, it is possible to develop a predictive model that can estimate the revenue a new movie is likely to make.

Clearly defining the problem statement:

Can we use movie budgets to predict movie revenues?

In this case, the question of whether movie budgets can be used to predict movie revenues is a tangible problem that can be tested. The hypothesis suggests that there is a relationship between the movie budget and revenue, but the extent and strength of this relationship are unknown.

To test this hypothesis, data on past movies’ budgets and revenues can be collected and analyzed to identify any patterns or trends. This information can then be used to develop a predictive model that can estimate a new movie’s potential revenue based on its budget. By clearly defining the problem statement, we can ensure that the project stays on track and that the results obtained are meaningful and relevant to the original question.

This can be measured using data and linear regression. In machine learning, the Revenue will be called the target, while the budget, the Feature (independent variable)

Step 2: Data Collection

Identify the data you need. In our case:

1. Data on features: Movie budgets in USD
2. Data on target: Movie Revenue in USD

Check Here for data: Data

Step 3: Data Cleaning

This step will focus on cleaning our dataset by removing unwanted data points. Specifically, we will remove rows that contain 0 values in the “Worldwide Gross ($)” field. This is important because such values might skew our analysis and lead to incorrect conclusions. To accomplish this task, we will use the csv module in Python provides a convenient way to read and write CSV files.

import csv

input_file = 'cost_revenue_dirty.csv'
output_file = 'cost_revenue_non0.csv'
field_name = 'Worldwide Gross ($)'

with open(input_file, 'r', newline='') as infile, open(output_file, 'w', newline='') as outfile:
    reader = csv.DictReader(infile)
    fieldnames = reader.fieldnames
    writer = csv.DictWriter(outfile, fieldnames=fieldnames)
    writer.writeheader()

    for row in reader:
        if row[field_name] != '$0':
            writer.writerow(row)

Open the input CSV file and read its content using the csv.DictReader class. Then creates a new output CSV file, where the cleaned data is written. We iterate through each row of the input file, and if the value in the "Worldwide Gross ($)" field is not 0, we write the row to the output file. This process effectively filters out any rows with a 0 Worldwide Gross, resulting in a clean dataset that we can use for further analysis.

This data-cleaning step is crucial for preparing our dataset for more accurate and reliable analysis. By eliminating the unwanted data points, we ensure that our subsequent findings are based on meaningful and relevant information. With the cleaned data in hand, we can now move on to the next step in our data analysis workflow.

Check the precious post: The Essential Steps to Approach a Data Science Problem: From Problem Formulation to Result Validation