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https://github.com/rakibhhridoy/webappclassifierstreamlit-python
Machine Learning Training and Testing in Web App. It provide good learning interface for experimenting with different hyper parameter tuning and compare different algorithms with each other without writing code repeatedly.
https://github.com/rakibhhridoy/webappclassifierstreamlit-python
classification hyperparameter-optimization hyperparameter-tuning logistic-regression machine-learning python random-forest streamlit support-vector-machines svm-classifier webapp
Last synced: about 1 month ago
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Machine Learning Training and Testing in Web App. It provide good learning interface for experimenting with different hyper parameter tuning and compare different algorithms with each other without writing code repeatedly.
- Host: GitHub
- URL: https://github.com/rakibhhridoy/webappclassifierstreamlit-python
- Owner: rakibhhridoy
- Created: 2020-08-14T06:56:48.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2020-08-14T11:26:15.000Z (over 4 years ago)
- Last Synced: 2024-11-06T15:27:04.985Z (3 months ago)
- Topics: classification, hyperparameter-optimization, hyperparameter-tuning, logistic-regression, machine-learning, python, random-forest, streamlit, support-vector-machines, svm-classifier, webapp
- Language: Python
- Homepage: https://rakibhhridoy.gihub.io
- Size: 272 KB
- Stars: 2
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# *Web App Classifiers Streamlit & Python*
### Available Machine learning Algorithms
- [x] Support Vector Machine
- [x] Logistic Regression
- [x] Random Forest Classifier
- [ ] KNN -On task
- [ ] Decision Tree -On task
- [ ] Naive Bayes -On task
### Available Metrics
- [x] Confusion Matrix
- [x] Accuracy
- [x] Precision
- [x] Recall
- [x] AUC ROC
- [x] ROC Curve
- [x] Precision Recall Curve
- [ ] Validation Curve -On task
- [ ] Learning Curve -On task
### Set up guide
> First Get the repository files Downloading or Clonning the repository
> Now open the folder using any ide or editor and select python. Now navigate to console and type
```bash
streamlit run app.py
```
> It will open in localhost server. Now you can use it easily.
### Customize Heading and Title of your Task
```python
def main():
st.title('Binary Classification Web App')
st.sidebar.title('Binary Classifier App')
st.markdown("Are Your Mashroom poisonous?")
st.sidebar.markdown("Are Your Mashroom poisonous?")
@st.cache(persist = True)
def load_data():
df = pd.read_csv('data/mushrooms.csv')
le = LabelEncoder()
for col in df.columns:
df[col] = le.fit_transform(df[col])
return df
```
Change the title,sidebar title,markdown and sidebar markdown according to your project.
### Environment
Make sure you have all necessery python library installed. For this projects you need
* Numpy
* Pandas
* Streamlit
* Scikit-learn
if you don't have these libraries go to python or anaconda prompt and paste these:
```bash
pip install numpy
pip install pandas
pip install streamlit
pip install sklearn
```
### Get touch with Me
Connect- [Linkedin](https://linkedin.com/in/rakibhhridoy)
Website- [RakibHHridoy](https://rakibhhridoy.github.io)