Complete Dataset consists of 2 CSV files. One of them is training and other is for testing your model.

Each CSV file has 133 columns. 132 of these columns are symptoms that a person experiences and last column is the prognosis.

These symptoms are mapped to 42 diseases you can classify these set of symptoms to.

You are required to train your model on training data and test it on testing data

'itching', 'skin_rash', 'nodal_skin_eruptions', 'continuous_sneezing', 'shivering', 'chills', 'joint_pain', 'stomach_pain', 'acidity', 'ulcers_on_tongue', 'muscle_wasting', 'vomiting', 'burning_micturition', 'spotting_ urination' ,'fatigue',

'weight_gain', 'anxiety' ,'cold_hands_and_feets' ,'mood_swings', 'weight_loss' ,'restlessness', 'lethargy',

'patches_in_throat', 'irregular_sugar_level', 'cough', 'high_fever', 'sunken_eyes','breathlessness', 'sweating',

'dehydration' ,'indigestion', 'headache', 'yellowish_skin', 'dark_urine' ,'nausea' ,'loss_of_appetite',

'pain_behind_the_eyes', 'back_pain','constipation', 'abdominal_pain', 'diarrhoea', 'mild_fever', 'yellow_urine',

'yellowing_of_eyes', 'acute_liver_failure' ,'fluid_overload', 'swelling_of_stomach', 'swelled_lymph_nodes',

'malaise', 'blurred_and_distorted_vision', 'phlegm' ,'throat_irritation', 'redness_of_eyes', 'sinus_pressure',

'runny_nose', 'congestion', 'chest_pain', 'weakness_in_limbs', 'fast_heart_rate', 'pain_during_bowel_movements',

'pain_in_anal_region', 'bloody_stool', 'irritation_in_anus', 'neck_pain', 'dizziness', 'cramps', 'bruising',

'obesity', 'swollen_legs', 'swollen_blood_vessels', 'puffy_face_and_eyes', 'enlarged_thyroid', 'brittle_nails',

'swollen_extremeties', 'excessive_hunger', 'extra_marital_contacts' ,'drying_and_tingling_lips', 'slurred_speech',

'knee_pain', 'hip_joint_pain', 'muscle_weakness' ,'stiff_neck', 'swelling_joints', 'movement_stiffness', 'spinning_movements',

'loss_of_balance', 'unsteadiness', 'weakness_of_one_body_side', 'loss_of_smell', 'bladder_discomfort',

'foul_smell_of urine', 'continuous_feel_of_urine', 'passage_of_gases', 'internal_itching', 'toxic_look_(typhos)',

'depression', 'irritability', 'muscle_pain', 'altered_sensorium', 'red_spots_over_body', 'belly_pain',

'abnormal_menstruation', 'dischromic _patches', 'watering_from_eyes', 'increased_appetite', 'polyuria', 'family_history',

'mucoid_sputum', 'rusty_sputum', 'lack_of_concentration', 'visual_disturbances', 'receiving_blood_transfusion',

'receiving_unsterile_injections', 'coma', 'stomach_bleeding', 'distention_of_abdomen', 'history_of_alcohol_consumption',

'fluid_overload.1', 'blood_in_sputum', 'prominent_veins_on_calf', 'palpitations', 'painful_walking', 'pus_filled_pimples', 'blackheads', 'scurring', 'skin_peeling',

'silver_like_dusting', 'small_dents_in_nails', 'inflammatory_nails', 'blister', 'red_sore_around_nose', 'yellow_crust_ooze'​

In prognosis we have 41 diseases as result:

'(vertigo) Paroymsal  Positional Vertigo', 'AIDS', 'Acne', 'Alcoholic hepatitis', 'Allergy', 'Arthritis', 'Bronchial Asthma', 'Cervical spondylosis', 'Chicken pox', 'Chronic cholestasis', 'Common Cold', 'Dengue', 'Diabetes ', 'Dimorphic hemmorhoids(piles)', 'Drug Reaction', 'Fungal infection', 'GERD', 'Gastroenteritis', 'Heart attack', 'Hepatitis B', 'Hepatitis C', 'Hepatitis D', 'Hepatitis E', 'Hypertension ', 'Hyperthyroidism', 'Hypoglycemia', 'Hypothyroidism', 'Impetigo', 'Jaundice', 'Malaria', 'Migraine', 'Osteoarthristis', 'Paralysis (brain hemorrhage)', 'Peptic ulcer diseae', 'Pneumonia', 'Psoriasis', 'Tuberculosis', 'Typhoid', 'Urinary tract infection', 'Varicose veins','hepatitis A'

# importing the library

import pandas as pd​

Reading Training Dataset

# Reading Training Dataset

df = pd.read_csv("training_data.csv")

# Checking shape of Dataset

df.shape

(4920, 134)

# Storing prognosis(prediction column) in y_train dataframe

y_train =df["prognosis"]



y_train.head(50)

# deleting prediction column as we have stored in y_train

del df["prognosis"]



# Unnamed column as it is of no use to us.

del df["Unnamed: 133"]​

df.head()

# Checking the NULL Values

df.isnull().sum()

# Storing training dataset in X

X = df



# Stroing prediction column in Y

Y = y_train​

# importing sklearn library for train test slpitting

from sklearn.model_selection import train_test_split

X_train, X_test,Y_train,Y_test = train_test_split(X,Y,test_size=0.1,stratify=Y,random_state=2)​

# Checking the shape of test train dataset

print(X.shape, X_train.shape, X_test.shape)

from sklearn.ensemble import RandomForestClassifier

classifier = RandomForestClassifier(n_estimators = 200, criterion = 'entropy', random_state = 0)

classifier.fit(X_train,Y_train)



prediction_rfc = classifier.predict(X_test)



import sklearn.metrics as metrics



print('Confusion Matrix: Random Forest Classifier')

print(metrics.confusion_matrix(Y_test, prediction_rfc))

print('\nClassification Report:')

print(metrics.classification_report(Y_test, prediction_rfc))

# Reding the Testing Dataset

dft = pd.read_csv("test_data.csv")​

# Viweing Dataset

dft.head()

# Storing prediction column of testing dataset in y_test

y_test =dft["prognosis"]



# Cheking Dataset

y_test.head(3)

# Deleting the prognosis column from testing dataset

del dft["prognosis"]

dft.head(3)

# Doing prediction for testing dataset

prediction = clf.predict(dft)



# Printing values of prediction

print(prediction)



# Checking the accuracy of prediction

print("Accuracy: ", metrics.accuracy_score(prediction,y_test))