Projects | Kishan Mistri

Personalized Cancer Diagnosis

Tue, 20 Dec 2022 07:12:41 +0000

From the expert, the time to diagnosis of cancer takes a lot of time as it includes new studies/papers, which makes this a time-consuming and exhaustive process. With machine learning, we can fast-track the majority of scenarios and help the expert get updated details.

I have used below classical machine learning algorithms for the problem.

1: Naive Bayes

2: K Nearest Neighbors

3: Logistic Regression

4: Support Vector Machine (SVM)

5: Random Forest Classifier

6: StackedClassifier (Ensemble)

7: MaxVoting Classifier (Ensemble)

As you might know, these algorithms have their limitation and advantages, I have tried to incorporate the best use of them by remediating the problems. Like

The curse of dimensionality has been addressed by Response Coding.
Class imbalance can be tuned with stratified splits and using the Class weight parameter whenever exploitable.
Compute intensive Hyper-Tuning with parallelism when needed.
At last, the beautiful interface & ton of integration of streamlit used.

Will they be present for Next workout?

Wed, 26 Oct 2022 08:13:00 +0000

M5 Accuracy - Walmart Sales prediction

Mon, 04 Apr 2022 09:45:37 +0000

The primary objective of this study is to forecast/predict sales accurately for the item-unit deals for Walmart based on store sales data provided for three US states (California, Texas, and Wisconsin). To perform expectations on different items sold in Walmart, machine learning methods have been actualized beside the conventional strategies to extend the exactness. Three diverse machine learning models are utilized to figure out daily deals for taking after 28 days. The primary problem at hand is to predict the price from historical data.

Music Genre Classification

Mon, 17 Jul 2017 12:00:00 +0000