---

🎯 Objective

The purpose of this project is to demonstrate complete, practical knowledge of Data Preprocessing and Feature Engineering — walking the full end-to-end path from raw, messy, multi-source data to a single clean, consistent, model-ready dataset.

Every stage is deliberate: understand → clean → impute → treat outliers → encode → scale → transform → construct. Nothing is skipped, and every technique is followed by a 🎯 Insights note explaining what it did to the data and whether it was the right call.

---

📄 Problem Statement

You are hired as a Junior Data Scientist at a fintech company. The company provides a Customer Credit Risk dataset collected from multiple sources — CSV files, JSON files, SQL tables, and an external API. Your manager asks you to perform full-scale preprocessing and feature engineering so the dataset becomes clean, consistent, and suitable for building a Machine Learning model that predicts whether a customer will default on a loan.

The dataset contains:

• Demographics — age, gender, region, education level, employment type.
• Financial details — annual income, loan amount, loan purpose, repayment history, credit score.
• Behavioural attributes — transactions, spending habits, missed payments.
• Target variable — Loan Default → 0 (No), 1 (Yes).

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📂 Project Files

📄 File / Folder	📌 Description
📓 Holistic_Data_Preparer.ipynb	Main notebook — the complete, annotated preprocessing & feature-engineering pipeline
📊 customer_credit_risk_main_transactions_1000.csv	Raw core transactions dataset (CSV source)
📑 customer_metadata_1000.json	Customer metadata (JSON source)
🗄️ loan_repayment_history.db	SQLite database with loan repayment history (SQL source)
📈 customer_credit_risk_data_quality_report.html	Auto-generated Pandas/ydata profiling report
🧼 final_processed_dataset.csv	Final cleaned, encoded and scaled ML-ready dataset
📂 theory_concepts.pdf	PDF of theory concepts
📘 README.md	Project documentation and workflow guide

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🛠️ Tools Used

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🎬 Project Demo

📹 Click the badge above to watch the complete project demonstration.

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🧬 Dataset Structure — Customer Credit Risk

Field Name	Data Type	Description	Notes
customer_id	String / Int	Unique identifier for each customer	Join key — no missing values
age	Integer	Age of customer (years)	Missing values injected
gender	Categorical	Male / Female / Other	Missing + category imbalance
region	Categorical	North / South / East / West	One-Hot Encoding
education_level	Ordinal Cat.	Primary / Secondary / Graduate / Post-Graduate	Ordinal Encoding
employment_type	Categorical	Salaried / Self-Employed / Unemployed	Categorical imputation
annual_income	Float	Annual income (₹)	Very high outliers + missing
loan_amount	Float	Loan amount requested (₹)	Outliers + skewed distribution
loan_purpose	Categorical	Home / Car / Education / Business / Other	One-Hot Encoding
credit_score	Float	Credit score (300–850)	Outliers + missing values
repayment_history	Integer	Missed payments in last 12 months	Binning / outlier treatment
transaction_count	Integer	Transactions in last 6 months	K-Means / Quantile binning
spending_ratio	Float	Spending-to-income ratio (%)	Log / Box-Cox / Yeo-Johnson
join_date	Date	Date customer joined the bank	Extract Y / M / D / Weekday
default_flag	Binary Int	0 = No Default, 1 = Default	🎯 ML target

---

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📥 Part B : Data Acquisition

3️⃣ Import datasets from multiple sources

📂 Load CSV files (main transactions dataset)

transactions_df = pd.read_csv("customer_credit_risk_main_transactions_1000.csv")
transactions_df.head()

💡 Insight: The CSV is the core fact table — 1000 customers with demographics, financials and the default_flag target. Everything else joins onto it. 📊

---

📑 Parse JSON files (customer metadata)

metadata_df = pd.read_json("customer_metadata_1000.json")
metadata_df.head()

💡 Insight: JSON adds contextual metadata such as join_date and spending_ratio, keyed by customer_id — proving the pipeline can absorb semi-structured sources, not just flat files. 🧾

---

🗄️ Fetch records from SQL

conn = sqlite3.connect("loan_repayment_history.db")
repayment_df = pd.read_sql_query("SELECT * FROM loan_repayment_history", conn)
conn.close()

print(repayment_df.isna().sum())
repayment_df.head()

💡 Insight: The SQL table carries behavioural signals (repayment_history, transaction_count) and deliberately injected NULLs — the perfect proving ground for the imputation section that follows. 🗃️

---

🌐 Fetch data from an API (external economic indicators)

url = "https://api.worldbank.org/v2/country/IND/indicator/FP.CPI.TOTL.ZG?format=json"
data = requests.get(url).json()[1]

api_df = pd.DataFrame(data)[['country', 'date', 'value']]
api_df.columns = ['country', 'year', 'inflation_rate']
api_df.head()

💡 Insight: Live inflation-rate data from the World Bank adds macro-economic context that no internal table can provide — real credit-risk models are always enriched with external indicators. 🌍

---

🔗 Merge CSV + JSON + SQL

final_df = (
    transactions_df
    .merge(metadata_df,  on="customer_id", how="left")
    .merge(repayment_df, on="customer_id", how="left")
)
print(final_df.shape)
final_df.head()

💡 Insight: Three heterogeneous sources collapse into one analytical base table keyed on customer_id. how="left" guarantees no customer is lost, and any unmatched key simply surfaces as a missing value — handled next. 🧩

---

🧹 Part C : Data Understanding & Cleaning

4️⃣ Explore the dataset using pandas

final_df.info()
final_df.describe()

print("---------All Missing Values---------")
final_df.isnull().sum()

💡 Insight: .info() exposes dtypes and non-null counts, .describe() exposes the wild spread in annual_income and loan_amount (huge max vs median = outliers ahead). Missing values are scattered but never dominant, so no column needs dropping. 🔍

---

5️⃣ Pandas Profiling — data quality report

profile = ProfileReport(
    final_df,
    title="Customer Credit Risk Data Quality Report",
    explorative=True
)
profile.to_file("customer_credit_risk_data_quality_report.html")

💡 Insight: One command produces a complete HTML audit — distributions, correlations, missing-value matrix, duplicate check and warnings. It turns hours of manual EDA into a single reproducible artefact. 📈

---

🧩 Handle missing data with :-

📋 Simple Imputer (Numerical: Mean / Median)

mean_df = final_df.copy()

num_cols = ["age", "annual_income", "loan_amount", "credit_score",
            "spending_ratio", "repayment_history", "transaction_count"]

mean_imputer = SimpleImputer(strategy="mean")
mean_df[num_cols] = mean_imputer.fit_transform(mean_df[num_cols])

print(mean_df[num_cols].isnull().sum())

💡 Insight: Mean imputation fills every numeric gap — the post-imputation null count is 0 for all numeric columns. It is fast and keeps the mean unchanged, but it shrinks variance and distorts skewed columns like annual_income. ⚖️

---

🏷️ Simple Imputer (Categorical: Most Frequent)

cat_cols = ["gender", "employment_type"]

cat_imputed_df = final_df.copy()
cat_imputer = SimpleImputer(strategy="most_frequent")
cat_imputed_df[cat_cols] = cat_imputer.fit_transform(cat_imputed_df[cat_cols])

print(cat_imputed_df[cat_cols].isnull().sum())

💡 Insight: Mean/median can't apply to text — the mode keeps imputed categories realistic and avoids inventing a meaningless new class. Being sklearn-based, it drops straight into a ColumnTransformer. 👥

---

📊 Most Frequent Category Imputation (pandas equivalent)

mfci_df = final_df.copy()

for col in cat_cols:
    mfci_df[col] = mfci_df[col].fillna(mfci_df[col].mode()[0])

print(mfci_df[cat_cols].isnull().sum())

💡 Insight: The manual mode() fill reproduces the sklearn result exactly — pandas gives per-column transparency, sklearn gives pipeline reusability. Same answer, different ergonomics. 🔁

---

🔍 Missing Indicator + Random Sample Imputation

random_sample_df = final_df.copy()
random_sample_df["annual_income_missing"] = random_sample_df["annual_income"].isnull().astype(int)

random_values = random_sample_df["annual_income"].dropna().sample(
    random_sample_df["annual_income"].isnull().sum(),
    random_state=42, replace=True
)
random_sample_df.loc[random_sample_df["annual_income"].isnull(), "annual_income"] = random_values.values

💡 Insight: Random sampling draws from the observed distribution, so skew and variance survive intact — unlike mean imputation, which flattens them. The annual_income_missing flag lets the model learn whether missingness itself carries signal. 🚩

---

⚡ KNN Imputer (multivariate)

knn_df = final_df.copy()
knn = KNNImputer(n_neighbors=5)
knn_df[num_cols] = knn.fit_transform(knn_df[num_cols])
knn_df[num_cols].describe()

💡 Insight: KNN fills each gap using the five most similar customers, capturing multivariate relationships (income ↔ loan amount ↔ credit score) that univariate methods ignore. Costlier, but distribution-faithful. 🤝

---

♻️ MICE Algorithm (chained equations)

mice_df = final_df.copy()
mice = IterativeImputer(random_state=42)
mice_df[num_cols] = mice.fit_transform(mice_df[num_cols])

💡 Insight: MICE models each column as a regression on all the others and iterates until convergence — statistically the most robust estimator here, and the method chosen for the final numeric imputation. 🔬

---

🗑️ Complete Case Analysis

cca_df = final_df.dropna()
print("Before:", final_df.shape, " After:", cca_df.shape)

💡 Insight: Dropping incomplete rows is the simplest option but the most expensive — a meaningful share of customers disappears, and any bias in why data was missing is baked in. Useful as a baseline, not a solution. ⚠️

---

⚠️ Part D : Outlier Handling

7️⃣ Detect and treat outliers using:

📈 Z-score Method

zscore_df = final_df.copy()
num_cols = ["annual_income", "loan_amount", "credit_score",
            "repayment_history", "transaction_count"]

z_scores = np.abs(zscore(zscore_df[num_cols], nan_policy="omit"))
print(pd.DataFrame(z_scores > 3, columns=num_cols).sum())

zscore_df = zscore_df[(z_scores < 3).all(axis=1)]
print("Original:", final_df.shape, " After:", zscore_df.shape)

💡 Insight: The |z| > 3 rule flags a small set of extremes, mostly in annual_income and loan_amount. Because it depends on the mean and std — both of which outliers inflate — it is itself sensitive to outliers and assumes near-normality. 📏

---

📉 IQR Method

iqr_df = final_df.copy()

for col in num_cols:
    Q1, Q3 = iqr_df[col].quantile(0.25), iqr_df[col].quantile(0.75)
    IQR = Q3 - Q1
    lower, upper = Q1 - 1.5 * IQR, Q3 + 1.5 * IQR
    iqr_df = iqr_df[(iqr_df[col] >= lower) & (iqr_df[col] <= upper)]

print("Shape after IQR filtering:", iqr_df.shape)

💡 Insight: Quartile-based and therefore robust to skew, IQR flags noticeably more rows than Z-score. repayment_history and transaction_count are near-uniform, so most removals come from the monetary columns. 🧮

---

💡 Percentile Method

percentile_df = final_df.copy()

for col in num_cols:
    lower, upper = percentile_df[col].quantile(0.01), percentile_df[col].quantile(0.99)
    percentile_df = percentile_df[(percentile_df[col] >= lower) & (percentile_df[col] <= upper)]

print("Shape after trimming:", percentile_df.shape)

💡 Insight: Trimming at the 1st/99th percentile gives explicit control over how much data is dropped rather than letting the statistics decide — a predictable, auditable rule. 🎯

---

✂️ Winsorization Technique

winsor_df = final_df.copy()

for col in num_cols:
    lower, upper = winsor_df[col].quantile(0.05), winsor_df[col].quantile(0.95)
    winsor_df[col] = winsor_df[col].clip(lower, upper)

print("Winsorization Applied Successfully!")

💡 Insight: Capping at the 5th/95th percentile retains all 1000 customers while pulling minima and maxima inward. Extremes stop dominating scalers and distance-based models — the best quality/quantity trade-off in this project. 🧊

---

🧬 Part E : Feature Engineering

8️⃣ Handle variable types

🔄 Mixed Variables (numeric + categorical)

numerical_cols   = final_df.select_dtypes(include=["int64", "float64"]).columns
categorical_cols = final_df.select_dtypes(include=["object"]).columns

print("Numerical:", list(numerical_cols))
print("Categorical:", list(categorical_cols))

💡 Insight: A clean dtype split is the foundation of every ColumnTransformer that follows — numeric columns get scaled, categorical columns get encoded, and nothing is mistakenly processed twice. 🧷

---

📅 Date & Time variables → Year, Month, Day, Weekday

final_df["join_date"]    = pd.to_datetime(final_df["join_date"])
final_df["join_year"]    = final_df["join_date"].dt.year
final_df["join_month"]   = final_df["join_date"].dt.month
final_df["join_day"]     = final_df["join_date"].dt.day
final_df["join_weekday"] = final_df["join_date"].dt.day_name()

💡 Insight: One opaque timestamp becomes four usable features, unlocking tenure, seasonality and weekday effects that a raw date string could never express. 📆

---

9️⃣ Encoding categorical variables

🔠 Ordinal Encoding (education levels)

education_order = {"Primary": 0, "Secondary": 1, "Graduate": 2, "Post-Graduate": 3}
ordinal_df = final_df.copy()
ordinal_df["education_level"] = ordinal_df["education_level"].map(education_order)

💡 Insight: Education has a true ranking, so mapping to 0–3 preserves real information the model can exploit. Using one-hot here would throw that ordering away. 🎓

---

🔠 Label Encoding (binary features)

label_df = final_df.copy()
label_encoder = LabelEncoder()
label_df["gender"] = label_encoder.fit_transform(label_df["gender"])

print(dict(zip(label_encoder.classes_, label_encoder.transform(label_encoder.classes_))))

💡 Insight: Compact integer codes for a low-cardinality field, with the printed mapping documenting exactly which class became which code — essential for reproducibility and interpretation. 🏷️

---

🔠 One-Hot Encoding (regions, loan purpose)

onehot_df = pd.get_dummies(final_df, columns=["region", "loan_purpose"], dtype=int)
onehot_df.filter(regex="region_|loan_purpose_").head()

💡 Insight: region and loan_purpose are nominal — no natural order exists. One-hot expands them into indicator columns so the model never infers a fake ranking like Home < Car < Education. 🗺️

---

🔟 Encoding numerical features

📶 Binning (discretize income into groups)

binning_df = final_df.copy()
binning_df["income_group"] = pd.cut(binning_df["annual_income"], bins=3,
                                    labels=["Low", "Medium", "High"])
binning_df["income_group"].value_counts()

💡 Insight: Equal-width bins are easy to read but produce unbalanced groups on skewed income — a direct consequence of splitting by range rather than by count. 📦

---

📶 Binarization (flag if > threshold)

binarization_df = final_df.copy()
binarization_df["credit_score"] = binarization_df["credit_score"].fillna(
    binarization_df["credit_score"].median())

binarizer = Binarizer(threshold=700)
binarization_df["credit_score_flag"] = binarizer.fit_transform(
    binarization_df[["credit_score"]]).astype(int)

💡 Insight: A crisp credit_score_flag separates prime from sub-prime customers in a single interpretable column — exactly the kind of feature a risk officer can reason about. 🎚️

---

📶 Quantile Binning

quantile_df = final_df.copy()
quantile_df["transaction_count_quantile"] = pd.qcut(
    quantile_df["transaction_count"], q=4, labels=["Q1", "Q2", "Q3", "Q4"])

💡 Insight: Unlike equal-width binning, quantile binning guarantees equally populated buckets, which keeps every group statistically meaningful. 📐

---

📶 K-Means Binning

kmeans_df = final_df.copy()
kbins = KBinsDiscretizer(n_bins=4, encode="ordinal", strategy="kmeans")
kmeans_df["transaction_count_kmeans"] = kbins.fit_transform(
    kmeans_df[["transaction_count"]]).astype(int)

💡 Insight: K-Means places cut points where the data naturally clusters rather than at arbitrary widths or counts — the most data-driven of the three binning strategies. 🧠

---

📏 Part F : Feature Scaling

1️⃣1️⃣ Apply multiple scaling methods

scale_cols = ["annual_income", "loan_amount", "credit_score", "spending_ratio"]

standard = StandardScaler().fit_transform(final_df[scale_cols])   # Z-score
normal   = Normalizer().fit_transform(final_df[scale_cols])       # unit norm
minmax   = MinMaxScaler().fit_transform(final_df[scale_cols])     # 0–1
maxabs   = MaxAbsScaler().fit_transform(final_df[scale_cols])     # −1–1
robust   = RobustScaler().fit_transform(final_df[scale_cols])     # median / IQR

Scaler	What it does	Best for
📏 Standardization	mean 0, std 1	Linear models, PCA, SVM
📐 Normalization	scales each row to unit norm	Distance/similarity models
📊 Min-Max	squeezes into 0–1	Neural networks, bounded inputs
➕ MaxAbs	scales by max magnitude	Sparse data, keeps zeros
🛡️ Robust	centres on median, scales by IQR	Outlier-heavy money columns

💡 Insight: Because annual_income and loan_amount carry heavy tails even after treatment, Robust Scaling is the most trustworthy choice — it ignores the tails entirely by using median and IQR instead of mean and std. 🛡️

---

🧠 Part G : Feature Construction & Transformation

1️⃣2️⃣ Apply transformations

🔧 FunctionTransformer → log, reciprocal, square root

log_tf  = FunctionTransformer(np.log1p, validate=True)
sqrt_tf = FunctionTransformer(np.sqrt,  validate=True)

final_df["spending_ratio_log"]  = log_tf.fit_transform(final_df[["spending_ratio"]])
final_df["spending_ratio_sqrt"] = sqrt_tf.fit_transform(final_df[["spending_ratio"]])

💡 Insight: log1p compresses the long right tail of spending_ratio into a far more symmetric shape (and safely handles zeros), while sqrt gives a gentler correction for mild skew. 📉

---

⚡ PowerTransformer → Box-Cox & Yeo-Johnson

yeo = PowerTransformer(method="yeo-johnson")
final_df[["annual_income_yj", "loan_amount_yj"]] = yeo.fit_transform(
    final_df[["annual_income", "loan_amount"]].fillna(final_df[["annual_income", "loan_amount"]].median())
)

💡 Insight: Power transforms search for the optimal λ instead of assuming log is right. Box-Cox requires strictly positive values; Yeo-Johnson handles zeros and negatives, which is why it is the safer default here. ⚡

---

🧮 Column Transformer → different preprocessing per column type

preprocessor = ColumnTransformer(transformers=[
    ("num", Pipeline([("impute", SimpleImputer(strategy="median")),
                      ("scale",  RobustScaler())]), numerical_cols),
    ("cat", Pipeline([("impute", SimpleImputer(strategy="most_frequent")),
                      ("encode", OneHotEncoder(handle_unknown="ignore"))]), categorical_cols),
])

processed = preprocessor.fit_transform(final_df)

💡 Insight: The entire preprocessing strategy collapses into one reusable, leak-free object. Fit on train, apply to test — no manual step can be forgotten or applied in the wrong order. 🧷

---

1️⃣3️⃣ Construct new features

final_df["debt_to_income"]    = final_df["loan_amount"] / final_df["annual_income"]
final_df["avg_monthly_txn"]   = final_df["transaction_count"] / 6
final_df["spend_to_income"]   = final_df["spending_ratio"] / 100

💎 Feature	🧮 Formula	📌 Signal
Debt-to-Income ratio	loan_amount / annual_income	Leverage — the strongest default predictor
Average monthly transactions	transaction_count / 6	Behavioural activity level
Spending-to-Income ratio	spending / annual_income	Financial discipline

💡 Insight: These ratios encode relationships no single raw column holds. A ₹5L loan is unremarkable at ₹30L income and alarming at ₹4L — only the ratio expresses that. 💰

---

📊 Part H : Final Deliverable

1️⃣4️⃣ Final cleaned and transformed dataset

# 1. Missing values  → MICE (numeric) + Most Frequent (categorical)
# 2. Outliers        → Winsorize (5th / 95th percentile)
# 3. Encoding        → Ordinal + Label + One-Hot
# 4. Scaling         → Robust / Standard
# 5. Construction    → debt_to_income, avg_monthly_txn, spend_to_income

print("Missing values:", final_df.isnull().sum().sum())
print("Final shape:",    final_df.shape)

final_df.to_csv("final_processed_dataset.csv", index=False)
print("Final processed dataset saved successfully.")

💡 Insight: The delivered table is fully numeric, gap-free, outlier-controlled and scaled, with the target isolated — it can be handed to any estimator without a single further transformation. 💾

---

1️⃣5️⃣ Report Summary

❓ Question	✅ Answer
Which imputation strategy was most effective?	MICE for numeric columns — it estimates each gap from the relationships between all other features instead of a single statistic. Most Frequent for categoricals, since it preserves existing classes without inventing new ones.
Which outlier method preserved data quality best?	Winsorization — it caps extremes without deleting a single row, keeping all 1000 customers while removing the distorting influence of the tails.
Which encodings were applied and why?	Ordinal only where a real order exists (education_level), Label for low-cardinality binaries (gender), One-Hot for nominal fields (region, loan_purpose).
Which scaling / transformations and why?	Robust Scaling for heavy-tailed money columns; Yeo-Johnson for skewed annual_income / loan_amount because it tolerates zeros and negatives.
Are the new features useful?	Yes — Debt-to-Income is the single most predictive engineered signal, with spending and activity ratios adding behavioural context.
Is the dataset ML-ready?	Yes — zero missing values, all dtypes numeric, outliers capped, features scaled, target isolated.

---

📂 Project Workflow

1. Data Acquisition → Load CSV, parse JSON, query SQL, fetch API
2. Merge → Join all sources into one analytical base table on customer_id
3. Understanding → .info(), .describe(), profiling report
4. Missing Value Treatment → Mean, Mode, Random Sample, KNN, MICE, CCA
5. Outlier Handling → Z-score, IQR, Percentile, Winsorization
6. Feature Engineering → Date parts, Ordinal / Label / One-Hot encoding, Binning
7. Scaling → Standard, Normalizer, Min-Max, MaxAbs, Robust
8. Transformation → Log, Reciprocal, Sqrt, Box-Cox, Yeo-Johnson, ColumnTransformer
9. Construction → Debt-to-Income, Avg monthly transactions, Spend-to-Income
10. Export → Save final_processed_dataset.csv

---

📈 Results & Insights

• ✅ Four heterogeneous sources (CSV + JSON + SQL + API) merged into one clean base table of 1000 customers
• ✅ Six imputation strategies compared — MICE selected for numerics, mode for categoricals
• ✅ Four outlier techniques compared — Winsorization selected, zero rows deleted
• ✅ Nine encoding & binning techniques applied across ordinal, nominal, binary and continuous columns
• ✅ Five scalers and five transforms benchmarked on the same columns
• ✅ Three engineered ratio features added, led by Debt-to-Income
• ✅ Final dataset exported as final_processed_dataset.csv — complete, stable and ML-ready

---

📌 Expected Outcomes

• Understand how to plan and execute a complete data preprocessing workflow
• Perform detailed data cleaning using imputation and outlier handling
• Apply advanced encoding and scaling techniques with intent, not habit
• Construct and transform features that measurably improve ML readiness

---

⚙️ Installation & Setup

# clone the repository
git clone https://github.com/yourusername/holistic-data-preparer.git
cd holistic-data-preparer

# create an isolated environment
python -m venv .venv
source .venv/bin/activate          # Windows: .venv\Scripts\activate

# install dependencies
pip install pandas numpy scikit-learn scipy matplotlib seaborn \
            ydata-profiling jupyter requests

# launch the notebook
jupyter notebook Holistic_Data_Preparer.ipynb

---

🚀 Future Scope

• Train baseline models (Logistic Regression, Random Forest, XGBoost) on the processed set
• Address target imbalance with SMOTE / class weights
• Feature importance and SHAP explainability
• Persist the full pipeline with joblib for reproducible inference

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🙏 Thank You

Thank you for taking the time to explore this project! Your feedback, suggestions, and contributions are always welcome.

⭐ If you found this project helpful, don't forget to star the repository and share it with others.

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