AI-Powered Data Analysis & Automated Insights SaaS
An intelligent automated data processing platform supporting multi-format tabular files (CSV, XLS, JSON) with instant missing-value handling, Isolation Forest anomaly detection, statistical distributions, and natural-language executive report generation.
The Problem
Data analysts and non-technical stakeholders waste upwards of 15-20 hours every week manually parsing uncleaned CSV files, writing boilerplate summary statistics, detecting corrupt outliers, and drafting executive PowerPoint summaries.
The Solution
Engineered an automated end-to-end Python pipeline with Flask, Pandas, and Scikit-Learn. Users upload raw data files and receive cleaned tables, anomaly flags, distribution charts, and LLM-generated narrative insights in under two seconds.
System Architecture & Data Flow
Multipart Ingestion & Dtype Inference
Asynchronous file upload validating mime-types and streaming chunks. Automated heuristic data type deduction distinguishing datetime, categorical, and continuous numerical features.
Isolation Forest Anomaly Filtering
Multivariate outlier scoring using Scikit-Learn's Isolation Forest algorithm with adaptive contamination parameters to isolate corrupt records and statistical anomalies.
Automated Statistical Profiling
Computes mean, median, standard deviation, skewness, kurtosis, and correlation matrices rendered via Chart.js and Plotly.
AI Executive Summary & PDF Generator
Passes statistical aggregates into an optimized LLM prompt pipeline to generate human-readable business takeaways with one-click PDF and Excel export.
Core Preprocessing Engine
# Automated Data Ingestion, Profiling & Anomaly Isolation Engine
import pandas as pd
import numpy as np
from sklearn.ensemble import IsolationForest
from typing import Dict, Any
class DataProfiler:
def __init__(self, contamination: float = 0.03):
self.contamination = contamination
def analyze_dataset(self, file_path: str) -> Dict[str, Any]:
# Handle multiple file formats
if file_path.endswith('.csv'):
df = pd.read_csv(file_path)
elif file_path.endswith(('.xls', '.xlsx')):
df = pd.read_excel(file_path)
else:
df = pd.read_json(file_path)
numeric_cols = df.select_dtypes(include=[np.number]).columns
# Anomaly Detection using Isolation Forest
if len(numeric_cols) > 0:
clf = IsolationForest(contamination=self.contamination, random_state=42)
imputed_data = df[numeric_cols].fillna(df[numeric_cols].median())
df['is_outlier'] = clf.fit_predict(imputed_data)
outlier_count = int((df['is_outlier'] == -1).sum())
else:
outlier_count = 0
# Statistical Summary Aggregates
summary_stats = df[numeric_cols].describe().to_dict()
return {
"total_rows": len(df),
"total_columns": len(df.columns),
"missing_values": int(df.isna().sum().sum()),
"outliers_flagged": outlier_count,
"column_metrics": summary_stats
}"Majid engineered our automated transaction reconciliation and anomaly detection pipeline. What used to take our Karachi finance team 6 hours every day is now solved in under 3 seconds with 99.4% precision. Outstanding Pakistani talent."
