mltoolkit

💫 About Developer:

Banish J

🤖 AI & ML Developer | Data Science & Analytics Expert

🌐 Web Apps & IoT Skilled | Awesome UI Creator

💻 AI is my main focus! 👾

📞 Contact

☎️ 9444333914

📧 mail@banish.in

🌐 Banish

Core Concept

This python package is mde to simplify your ml tasks and make you to run large program at few lines of code it simplifies your program and imprpve your productivity.

About this Package

A Python package that automates ML workflows, trains multiple models with one line of code, and identifies the best performer for faster deployment.

Key Features:

✅ Single-Line Model Training - Train multiple ML models simultaneously with minimal code.

✅ Automated Accuracy Comparison – Generates performance tables and highlights the best model.

✅ Boosts Productivity – Reduces repetitive tasks, speeding up research and deployment.

Use Cases:

🔹 Quick Prototyping – Ideal for testing multiple algorithms in research or hackathons.

🔹 Automated Benchmarking – Compare models effortlessly for optimal performance.

🔹 Beginner-Friendly ML – Simplifies model training for students and new developers.

Why It Stands Out:

🚀 Saves Time – No manual coding for each model; instant results.

📊 Smart Decision-Making – Auto-selects the best model based on accuracy.

Algorithm accuracy table

from mltoolkit import Regression as regressor

  

acc_table, best_param = regressor.svm(independent, dependent)

acc_table

output

---

	C	linear	rbf	poly	sigmoid
1	C 10	0.022506	-0.08521	-0.082239	-0.099652
2	C 100	0.563729	-0.113243	-0.084659	-0.132517
3	C 500	0.64177	-0.10929	-0.064037	-0.582106
4	C 1000	0.669795	-0.102105	-0.032889	-2.022042
5	C 2000	0.767813	-0.090715	0.02603	-6.818809
6	C 3000	0.764471	-0.079182	0.083426	-14.702022
7	C 7000	0.734434	-0.028374	0.291094	-73.122034

how to use

from mltoolkit import Regression as regressor

  

acc_table, best_param = regressor._algname_(independent, dependent)

acc_table

• replace _algname_ with svm, decision_tree, random_forest, knn for Regression

• replace _algname_ with decision_tree, random_forest, knn for Classification

• replace Regression with classifier if its a classification problem statement

Model accuracy table

from mltoolkit import Regression as regressor

  

reg_report = regressor.fit_model(independent, dependent)

reg_report

output

---

	Metrics	Random Forest	Linear Regression	Poisson Regression	Decision Tree	Support Vector Machine	KNN
1	MSE	20770567.875901	32304679.499094	30757741.967819	45999841.979685	172821773.971895	112965815.146866
2	MAE 4557.473848	5683.720568	5545.966279	6782.318334	13146.169555	10628.537771
3	R2 2714.117549	3985.71256	3748.157793	3099.796517	8532.534486	7417.95403
4	RMSE 0.868069	0.794807	0.804632	0.707817	-0.097732	0.282462
5	R2ADJ	0.867407	0.793777	0.803653	0.706352	-0.103238	0.278863

how to use

from mltoolkit import Regression as regressor

  

reg_report = regressor.fit_model(independent, dependent)

reg_report

• replace Regression with classifier if its a classification problem statement

• replace fit_model with fit_save to save the best model