You will get a neat & clean dataset as per your preference with cleaning summery
Project details
You will get a clean dataset where I :-
Fix missing values (fill, drop, flag)
Remove duplicates (exact & fuzzy)
Format dates, numbers, text, and currency
Standardize inconsistent data entries
Convert data types (text ↔ date, number, etc.)
Detect and handle outliers
Clean and organize columns (rename, drop, reorder)
Merge or join multiple datasets / Excel sheets
Deliver clean data in Excel, CSV, or JSON
Optional Python script with a cleaning summary report
Create basic visualizations (bar, line, pie, etc.)
Data visualizations (matplotlib, seaborn, pandas)
Fix missing values (fill, drop, flag)
Remove duplicates (exact & fuzzy)
Format dates, numbers, text, and currency
Standardize inconsistent data entries
Convert data types (text ↔ date, number, etc.)
Detect and handle outliers
Clean and organize columns (rename, drop, reorder)
Merge or join multiple datasets / Excel sheets
Deliver clean data in Excel, CSV, or JSON
Optional Python script with a cleaning summary report
Create basic visualizations (bar, line, pie, etc.)
Data visualizations (matplotlib, seaborn, pandas)
Data Tool
PythonWhat's included $50
These options are included with the project scope.
$50
- Delivery Time 1 day
- Number of Revisions 1
- Number of Pages Mined/Scraped 35
- Number of Sources Mined/Scraped 22
About Aryan
AI & Machine Learning | Python programmer
Anantapur, India - 6:30 pm local time
Network Threat Classification using LightGBM
~ Built a machine learning pipeline for malicious network traffic detection using the
UNSW-NB15 cybersecurity dataset.
~ Performed data preprocessing, feature encoding, class balancing, and exploratory
analysis on large-scale network traffic data.
~ Trained and optimized a LightGBM classifier achieving 95% accuracy in detecting
malicious network activity on unseen samples.
~ Visualized attack distributions, traffic patterns, and anomaly trends using
Matplotlib and Seaborn.
Respiration Rate Prediction from PPG Signals
~ Developed a physiological signal-processing pipeline for respiration-rate estimation
from Photoplethysmography (PPG) signals using the BIDMC PhysioNet dataset.
~ Engineered time-domain, frequency-domain, and statistical features from windowed
biosignal segments with subject-wise cross-validation for robust generalization.
~ Trained and optimized Random Forest and LightGBM models, achieving MAE of
2.14 (RF) and 2.19 (LGBM) across unseen subjects.
~ Performed feature selection and model interpretation to evaluate clinical reliability
and noise robustness for wearable-health monitoring applications.
Logistic Regression from Scratch
~ Implemented logistic regression from first principles using NumPy, including BCE
loss, L2 regularization, and gradient descent optimization.
~ Validated the implementation against Scikit-learn on the Wisconsin Breast Cancer
dataset, achieving 97% test accuracy.
~ Visualized optimization dynamics and decision boundaries to analyze convergence
behavior and verify model correctness.
Steps for completing your project
After purchasing the project, send requirements so Aryan can start the project.
Delivery time starts when Aryan receives requirements from you.
Aryan works on your project following the steps below.
Revisions may occur after the delivery date.
Data cleaning and collection
I was given a task to clean the messy excel file consisting of the verses of Bhagvad gita and also add one more column, which consist of the corresponding explainations from a relevant source.