Cervical cancer is a type of cancer that occurs in the cells of the cervix. As one of the most dangerous diseases among women, analyzing risk factors would allow early diagnoses and prevention. In our project, we aim to analyze the risk factors of Cervical Cancer. We collected our data from Kaggle Risk Factors of Cervical Cancer and extracted the risk factors as our features. The four diagnoses of Cervical Cancer indicates whether a participant are diagnosed with cervical cancer is our target variable. To analyze our data, we combine three selection methods to select a subset of features from our original set. We used Seaborn countplots and heatmap to visualize our data. We applied four different classification algorithms to our data and picked the one with the highest accuracy and F-1 score for our model. To optimize our model, we used GridsearchCV to figure out the parameters that would yield the highest accuracy.