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app/main.py

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+
+from fastapi import FastAPI, HTTPException, File, UploadFile
+from fastapi.responses import FileResponse
+from app.services.preprocessing import data_quality, standardize_data_types, handle_missing_data, handle_outliers, generate_final_report, save_cleaned_data
+import pandas as pd
+import io
+import os
+
+app = FastAPI(title="Data Preprocessing")
+os.makedirs("output", exist_ok=True)
+
+@app.get("/")
+async def root():
+    return {"message": "Welcome to the Data Preprocessing API!"}
+
+@app.post("/preprocess_data/")
+async def upload_csv(upload_file: UploadFile = File(...)):
+    try:
+        if not upload_file.filename.endswith('.csv'):
+            raise HTTPException(status_code=400, detail="File must be in CSV format!")
+        content = await upload_file.read()
+        df = pd.read_csv(io.BytesIO(content), encoding_errors="replace")
+        if df.empty:
+            raise HTTPException(status_code=400, detail="File is empty, upload the correct file")
+
+        data_quality(df)
+        df = standardize_data_types(df)
+        df = handle_missing_data(df)
+        df = handle_outliers(df)
+
+        REPORT_PATH = "output/preprocessing_report.txt"
+        generate_final_report(df, REPORT_PATH)
+
+        CLEANED_DATA_PATH = "output/cleaned_dataset.csv"
+        save_cleaned_data(df, CLEANED_DATA_PATH)
+
+        return FileResponse(CLEANED_DATA_PATH, media_type="text/csv", filename="cleaned_dataset.csv")
+
+    except Exception as e:
+        raise HTTPException(status_code=400, detail=f"Error processing file: {str(e)}")

app/services/preprocessing.py

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+
+from sklearn.impute import SimpleImputer
+import pandas as pd
+import numpy as np
+import json
+
+def data_quality(df: pd.DataFrame):
+    df.drop_duplicates(inplace=True)
+    return df
+
+def standardize_data_types(df: pd.DataFrame) -> pd.DataFrame:
+    for col in df.columns:
+        if df[col].isin([True, False]).all():
+            continue
+        if df[col].dtype == 'object' and df[col].str.replace('.', '', 1).str.isnumeric().all():
+            df[col] = pd.to_numeric(df[col], errors='ignore')
+        try:
+            df[col] = pd.to_datetime(df[col], errors='coerce')
+            if df[col].notna().sum() == 0:
+                df[col] = df[col].astype(str)
+        except Exception:
+            pass
+        try:
+            if df[col].apply(lambda x: isinstance(x, str) and x.startswith("[") and x.endswith("]")).all():
+                df[col] = df[col].apply(json.loads)
+        except Exception:
+            pass
+        if df[col].dtype == 'object' and df[col].dropna().isin(["TRUE", "FALSE"]).all():
+            df[col] = df[col].map({"TRUE": True, "FALSE": False})
+        if df[col].dtype == 'object':
+            df[col] = df[col].astype(str)
+    df.fillna("", inplace=True)
+    return df
+
+def handle_missing_data(df: pd.DataFrame) -> pd.DataFrame:
+    numeric_col = df.select_dtypes(include=['number']).columns
+    if not numeric_col.empty:
+        df[numeric_col] = SimpleImputer(strategy='median').fit_transform(df[numeric_col])
+    categorical_col = df.select_dtypes(include=['object']).columns
+    if not categorical_col.empty:
+        df[categorical_col] = SimpleImputer(strategy='most_frequent').fit_transform(df[categorical_col])
+    return df
+
+def handle_outliers(df: pd.DataFrame) -> pd.DataFrame:
+    numeric_col = df.select_dtypes(include=['number','int64', 'float64']).columns
+    if not numeric_col.empty:
+        for col in numeric_col:
+            Q1 = df[col].quantile(0.25)
+            Q3 = df[col].quantile(0.75) 
+            IQR = Q3 - Q1
+            lower = Q1 - 1.5 * IQR
+            upper = Q3 + 1.5 * IQR
+            df[col] = df[col].apply(lambda x: lower if x < lower else upper if x > upper else x)
+    return df
+
+def generate_final_report(df: pd.DataFrame, file_path: str):
+    with open(file_path, "w") as file:
+        file.write("FINAL DATA PREPROCESSING REPORT\n")
+        file.write("=" * 50 + "\n\n")
+        missing = df.isnull().sum()
+        for col, count in missing.items():
+            file.write(f"{col}: {count} missing values\n")
+        file.write(f"Total Duplicate Rows: {df.duplicated().sum()}\n")
+        file.write("Preprocessing Completed Successfully!\n")
+
+def save_cleaned_data(df: pd.DataFrame, file_path: str):
+    df.to_csv(file_path, index=False)
+    return file_path