Heroku App :- https://forestcoverclassificationapp.herokuapp.com/
This project is designed to predict the FOREST COVER CLASSIFICATION using Classification Analysis with Python, FLASK, HTML, SQL
A highly comprehensive analysis with all data cleaning, exploration, visualization, feature selection, model building, evaluation has been implemented.
Passionate towards nature and interest to explore various cover areas of forest, it motivates me to get and data and implement this project
The Code is written in Python 3.7. If you don't have Python installed you can find it here. If you are using a lower version of Python you can upgrade using the pip package, ensuring you have the latest version of pip. To install the required packages and libraries, run this command in the project directory after cloning the repository
pip install -r requirements.txt
1- EXporatory Data Analysis: Distribution,Outliers,POsitive Count,Negative Count,Zero Variance Variable
2- Data preparation: Feature Engineering and Scaling
3- Feature Engineering: Make Features usefull to model data
4- Feature Selection using RFE and Model Building
5- Data Modelling using Random Forest, XGBoost
CopyRight 2020 DHEERAJ KUMAR
https://www.gnu.org/licenses/gpl-3.0.en.html
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.