Mickey's Case Study : Acing Machine learning Project
Learn a detailed case study of our client Mickey from the University of Chicago who took our expert help to complete his Machine learning project.
Introduction: Mickey’s Challenge
Hey folks! Meet Mickey, an MS student at the University of Chicago. As part of his Data Science course, he had to complete a Machine Learning (ML) project that involved building a model to predict house prices based on different features like location, size, and market trends.
While Mickey had a basic understanding of Python, he quickly realized that working on a real ML project was much harder than he expected.
With just one week left before his deadline, Mickey felt completely lost. He searched online for a reliable tutoring company that could not only help him complete the project but also tutor him on ML concepts.
That’s when he found our website and reached out to us. We assigned him our experienced expert, Mr. Naoufal E., who guided him through the entire project while also teaching him key ML concepts.
With expert guidance, Mickey was able to overcome these challenges and successfully complete his project.
Problem: Stuck with Machine Learning Project
When Mickey started working on his project, he faced several major challenges that made it difficult for him to progress. He shared these concerns with our expert:
Data Preprocessing Issues – He wasn’t sure how to handle missing values and normalize data for better model performance.
Choosing the Right Algorithm – He was confused between Linear Regression and more advanced models like Random Forest.
Model Evaluation – He struggled to calculate and interpret performance metrics like Mean Squared Error (MSE) and R² Score.
Coding Errors & Debugging – He had difficulty splitting his dataset correctly and visualizing results using Matplotlib.
So, in this way he was truly clueless about this project and asked us for help from scratch.
Solution: How CodingZap Helped Mickey with his ML project
After understanding Mickey’s situation, we quickly assigned our senior Machine Learning expert, Mr. Naoufal, to work with him. Since Mickey not only wanted help with his project but also wanted to learn the process, we adopted a collaborative approach. Our expert scheduled a series of meetings with him to understand his understanding level for this project and made a plan for development and tutoring sessions.
Together, we worked step by step, making sure Mickey understood every part of the project.
Step 1: Data Cleaning and Preprocessing
Before training any model, it is really important to clean and prepare the dataset. Mickey’s dataset had missing values, and some features were not scaled properly, which could negatively impact the model. So, we started with the below steps:
- Handling Missing Values: We filled missing data using mean imputation for numerical features and mode imputation for categorical features.
Normalization: Normalized the numerical features to ensure the model performed consistently.
After that, he understood why data preprocessing is important and how it improves model accuracy.
from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler()
dataset['size_normalized'] = scaler.fit_transform(dataset[['size']])
Step 2: Selecting and Training the Model
Mickey was confused between Linear Regression and more advanced models like Random Forest. After analyzing the dataset, we recommended Random Forest Regression, which performs better when data has complex patterns and outliers.
from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
model = RandomForestRegressor(n_estimators=100)
model.fit(X_train, y_train)
Step 3: Evaluating the Model
We guided Mickey in calculating Mean Absolute Error (MAE) and R-squared score to evaluate his model’s accuracy.
Explained how to interpret the results and identify areas for improvement.
from sklearn.metrics import mean_absolute_error, r2_score
predictions = model.predict(X_test)
mae = mean_absolute_error(y_test, predictions)
r2 = r2_score(y_test, predictions)
print(f"Mean Absolute Error: {mae}")
print(f"R-squared Score: {r2}")
Step 4: Visualizing Results
After evaluating the model we helped Mickey create visualizations of predicted vs. actual house prices using Matplotlib: You can check the sample code below for better understanding.
import matplotlib.pyplot as plt
plt.scatter(y_test, predictions)
plt.xlabel("Actual Prices")
plt.ylabel("Predicted Prices")
plt.title("Actual vs Predicted Prices")
plt.show()
Results : Mickey’s Academic Success
Finally Mickey successfully completed his project on time, delivering a detailed report with clear visualizations. Our expert and team helped him earned an A grade, receiving praise from his professor for his detailed analysis and accurate model. Along the way, he gained practical knowledge in data preprocessing, model evaluation, and using Python libraries like scikit-learn and Matplotlib.
In this way we help students with their programming tasks and also provide then personalised tutoring session so that they can learn new concepts of programming and master their subjects.
Testimonial :
“I couldn’t have done it without CodingZap. They not only helped me completing my ML task but also made Machine Learning so much easier to understand. Thank you so much guys!” – Mickey, US
Conclusion :
At CodingZap, we’re passionate about helping students succeed in Machine Learning and other advanced topics Data Analysis, Data Science and AI models. From data preprocessing to building and evaluating ML models, our team ensures you learn while meeting your academic goals. You saw how we took baby steps in handling such complex ML projects for students and making them understand the concept fully.
So, If you’re stuck on a Machine Learning project like Mickey, let us help you deliver outstanding results. Get Machine learning project help now.