How did you clean and prepare the data?

Quality Thought – Best Data Science Training Institute in Hyderabad with Live Internship Program

If you're aspiring to become a skilled Data Scientist and build a successful career in the field of analytics and AI, look no further than Quality Thought – the best Data Science training institute in Hyderabad offering a career-focused curriculum along with a live internship program.

At Quality Thought, our Data Science course is designed by industry experts and covers the entire data lifecycle. The training includes:

Python Programming for Data Science

Statistics & Probability

Data Wrangling & Data Visualization

Machine Learning Algorithms

Deep Learning with TensorFlow and Keras

NLP, AI, and Big Data Tools

SQL, Excel, Power BI & Tableau

What makes us truly stand out is our Live Internship Program, where students apply their skills on real-time datasets and industry projects. This hands-on experience allows learners to build a strong project portfolio, understand real-world challenges, and become job-ready.

Why Choose Quality Thought?

✅ Industry-expert trainers with real-time experience

✅ Hands-on training with real-world datasets

✅ Internship with live projects & mentorship

✅ Resume preparation, mock interviews & placement assistance

✅ 100% placement support with top MNCs and startups

Whether you're a fresher, graduate, working professional, or career switcher, Quality Thought provides the perfect platform to master Data Science and enter the world of AI and analytics.

📍 Located in Hyderabad | 📞 Call now to book your free demo session and take the first step toward a data-driven future!.

🧹 Data Cleaning & Preparation Steps

1. Handling Missing Data

• Checked missing values using df.isnull().sum().

• Strategy:

  • Numerical columns (e.g., monthly charges) → filled with median (robust to outliers).

  • Categorical columns (e.g., contract type) → filled with mode (most frequent value).

  • For features like TotalCharges that had too many missing values → dropped the column if it added no predictive power.

2. Removing Duplicates & Inconsistent Records

• Removed duplicate customer IDs.

• Corrected inconsistent entries (e.g., Gender = “M” vs “Male” → standardized).

3. Encoding Categorical Variables

• Converted categories into numerical values:

  • One-hot encoding for non-ordinal features (contract type, internet service).

  • Label encoding for binary features (Yes/No fields like churn).

4. Feature Engineering

• Created new features:

  • Tenure Group (0–12 months, 13–24, etc.).

  • Average Monthly Spend = TotalCharges / Tenure.

• These improved interpretability and model performance.

5. Outlier Treatment

• Checked for extreme values in features like MonthlyCharges.

• Used IQR method to detect and cap outliers instead of removing them, since they often represent valid heavy users.

6. Scaling & Normalization

• Applied StandardScaler (z-score scaling) for numerical features to help distance-based models like Logistic Regression and SVM.

• Tree-based models (Random Forest, XGBoost) didn’t strictly need scaling, but I kept consistency.

7. Handling Imbalanced Data

• Churn cases were only ~20% of the dataset.

• Used SMOTE (Synthetic Minority Oversampling Technique) to balance classes.

• Also tested class weights in Logistic Regression & XGBoost.

✅ Result: After cleaning, I had a consistent, balanced, and model-ready dataset. This preparation significantly improved model accuracy (ROC-AUC from ~0.72 → 0.89).

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