Letter Recognition from a Photo with Machine Learning in Python.
Libraries used:
- Numpy
- Pandas
- Matplotlib
- Seaborn
Dataset contains the following:
- lettr capital letter (26 values from A to Z)
- x-box horizontal position of box (integer)
- y-box vertical position of box (integer)
- width width of box (integer)
- high height of box (integer)
- onpix total # on pixels (integer)
- x-bar mean x of on pixels in box (integer)
- y-bar mean y of on pixels in box (integer)
- x2bar mean x variance (integer)
- y2bar mean y variance (integer)
- xybar mean x y correlation (integer)
- x2ybr mean of x * x * y (integer)
- xy2br mean of x * y * y (integer)
- x-ege mean edge count left to right (integer)
- xegvy correlation of x-ege with y (integer)
- y-ege mean edge count bottom to top (integer)
- yegvx correlation of y-ege with x (integer)
Dataset is splitted into Train and Test Set so as to train our Model on Train Set and then use Test Set to evaluate the performance the Model.
Classification Algorithms used here:
- Logistic Regression
- SVM
- Random Forest
Model's performance is evaluated using:
- Confusuin Matrix
- Classification Report