<jupyter_start><jupyter_text>CALCULATION OF ERROR<jupyter_code>def Evaluationmatrix_DT(y_test, y_pred):
    print ('Mean Squared Error: '+ str(metrics.mean_squared_error(y_test,y_pred)))
    print ('Mean absolute Error: '+ str(metrics.mean_absolute_error(y_test,y_pred)))

Evaluationmatrix_DT(y_test,predict_popular_DT)

print('R square value : ',r2_score(y_test, predict_popular_DT))

from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test, predict_popular_DT)
print(cm)
index = ['Predicted Success','Predicted Unsuccess']  
columns = ['Actual Success','Actual Unsuccess']  
cm_df = pd.DataFrame(cm, columns, index)
plt.figure(figsize=(8,6))
sns.heatmap(cm_df, annot=True)
def Evaluationmatrix_RF(y_test, y_pred):
    print ('Mean Squared Error: '+ str(metrics.mean_squared_error(y_test,y_pred)))
    print ('Mean absolute Error: '+ str(metrics.mean_absolute_error(y_test,y_pred)))

Evaluationmatrix_RF(y_test,predict_popular_RF)

print('R square value : ',r2_score(y_test, predict_popular_RF))

from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test, predict_popular_RF.astype("int64"))
print(cm)
index = ['Predicted Success','Predicted Unsuccess']  
columns = ['Actual Success','Actual Unsuccess']  
cm_df = pd.DataFrame(cm, columns, index)
plt.figure(figsize=(8,6))
sns.heatmap(cm_df, annot=True)
def Evaluationmatrix_LR(y_test, y_pred):
    print ('Mean Squared Error: '+ str(metrics.mean_squared_error(y_test,y_pred)))
    print ('Mean absolute Error: '+ str(metrics.mean_absolute_error(y_test,y_pred)))

Evaluationmatrix_LR(y_test,predict_popular_LR)

print('R square value : ',r2_score(y_test, predict_popular_LR))

from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test, predict_popular_LR.astype("int64"))
print(cm)
index = ['Predicted Success','Predicted Unsuccess']  
columns = ['Actual Success','Actual Unsuccess']  
cm_df = pd.DataFrame(cm, columns, index)
plt.figure(figsize=(8,6))
sns.heatmap(cm_df, annot=True)
def Evaluationmatrix_NB(y_test, y_pred):
    print ('Mean Squared Error: '+ str(metrics.mean_squared_error(y_test,y_pred)))
    print ('Mean absolute Error: '+ str(metrics.mean_absolute_error(y_test,y_pred)))

Evaluationmatrix_NB(y_test,predict_popular_NB)

print('R square value : ',r2_score(y_test, predict_popular_NB))

from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test, predict_popular_NB.astype("int64"))
print(cm)
index = ['Predicted Success','Predicted Unsuccess']  
columns = ['Actual Success','Actual Unsuccess']  
cm_df = pd.DataFrame(cm, columns, index)
plt.figure(figsize=(8,6))
sns.heatmap(cm_df, annot=True)
def Evaluationmatrix_KNN(y_test, y_pred):
    print ('Mean Squared Error: '+ str(metrics.mean_squared_error(y_test,y_pred)))
    print ('Mean absolute Error: '+ str(metrics.mean_absolute_error(y_test,y_pred)))

Evaluationmatrix_KNN(y_test,predict_popular_KNN)

print('R square value : ',r2_score(y_test, predict_popular_KNN))

from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test, predict_popular_KNN.astype("int64"))
print(cm)
index = ['Predicted Success','Predicted Unsuccess']  
columns = ['Actual Success','Actual Unsuccess']  
cm_df = pd.DataFrame(cm, columns, index)
plt.figure(figsize=(8,6))
sns.heatmap(cm_df, annot=True)
def Evaluationmatrix_ANN(y_test, y_pred):
    print ('Mean Squared Error: '+ str(metrics.mean_squared_error(y_test,y_pred)))
    print ('Mean absolute Error: '+ str(metrics.mean_absolute_error(y_test,y_pred)))

Evaluationmatrix_ANN(y_test,predict_popular_ANN)

print('R square value : ',r2_score(y_test, predict_popular_ANN))

from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test, predict_popular_ANN.astype("int64"))
print(cm)
index = ['Predicted Success','Predicted Unsuccess']  
columns = ['Actual Success','Actual Unsuccess']  
cm_df = pd.DataFrame(cm, columns, index)
plt.figure(figsize=(8,6))
sns.heatmap(cm_df, annot=True)<jupyter_output>Mean Squared Error: 0.2877252252252252
Mean absolute Error: 0.2877252252252252
R square value :  -0.17304242173564655
[[546 220]
 [291 719]]
{'fraction_non_alphanumeric': 0.13013698630136986, 'fraction_numerical': 0.022504892367906065, 'mean_word_length': 8.106904231625835, 'pattern_counts': {'":': 0, '<': 4, '<?xml version=': 0, '>': 4, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '437028', 'license': 'MIT', 'n_tokens_mistral': 1569, 'n_tokens_neox': 1415, 'n_words': 269, 'path': 'Prediction.ipynb', 'repo_name': 'mousmee01/Data-Visualtisation-of-Google-Play-Store', 'size': '4088'}