- 📌 Data Enthusiast focused on insightful storytelling through data
- 🧬 Exploring the world of ML, LLMs, and AI automation
- 📊 Specializing in dashboard design, ETL, and data wrangling
- 💡 Current obsessions: LangChain, Hugging Face, TensorFlow
- 💬 Fun fact: I love debugging JOIN queries like solving puzzles 🧩
- Analyzed performance marketing data (CTR, CPC, ROAS, conversions) for Facebook Ads campaigns
- Created detailed revenue, campaign, and ROI reports to provide actionable insights
- Developed Tableau dashboards to track KPIs and campaign performance
- Managed and cleaned large datasets to ensure accurate reporting and analysis
- Automated KPI dashboards using Tableau & Excel
- Streamlined ETL pipelines using Python (Pandas, NumPy)
- Performed ad-hoc SQL queries for fast insights
- Reduced reporting time by 30% via automation
- ✅ Worked on Sales, Loan Prediction, and Marketing Analytics projects
- 🛠️ Tools used: MySQL, Scikit-learn, Power BI, Seaborn, Excel
Trends & Revenue insights powered by Matplotlib & Seaborn
# ML: Random Forest for Bank Loan Classification
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
# Load data
df = pd.read_csv("loan_data.csv")
X = df.drop("loan_status", axis=1)
y = df["loan_status"]
# Train/Test Split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# Model Training
model = RandomForestClassifier(random_state=42)
model.fit(X_train, y_train)
# Prediction & Accuracy
preds = model.predict(X_test)
print(f"Accuracy: {accuracy_score(y_test, preds):.2f}")
keep_trying()