├── KeepItSimple20.rar ├── ProjectChatBoatMedical.rar ├── README.md ├── Testing.csv ├── Training.csv ├── houseprice.csv ├── inclass.py ├── movie_recommendation.zip └── tweet.py /KeepItSimple20.rar: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/tesla-is/ML-Projects/f37256bc3451c6401d7c5a8a028171540957551c/KeepItSimple20.rar -------------------------------------------------------------------------------- /ProjectChatBoatMedical.rar: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/tesla-is/ML-Projects/f37256bc3451c6401d7c5a8a028171540957551c/ProjectChatBoatMedical.rar -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # ML-Projects 2 | Sample ML Projects 3 | -------------------------------------------------------------------------------- /Testing.csv: -------------------------------------------------------------------------------- 1 | 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,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,prognosis 2 | 1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Fungal infection 3 | 0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Allergy 4 | 0,0,0,0,0,0,0,1,1,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,GERD 5 | 1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,1,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Chronic cholestasis 6 | 1,1,0,0,0,0,0,1,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Drug Reaction 7 | 0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Peptic ulcer diseae 8 | 0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,AIDS 9 | 0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,1,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Diabetes 10 | 0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Gastroenteritis 11 | 0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Bronchial Asthma 12 | 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Hypertension 13 | 0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Migraine 14 | 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Cervical spondylosis 15 | 0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Paralysis (brain hemorrhage) 16 | 1,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,1,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Jaundice 17 | 0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,1,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Malaria 18 | 1,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Chicken pox 19 | 0,1,0,0,0,1,1,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,1,1,1,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Dengue 20 | 0,0,0,0,0,1,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,1,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Typhoid 21 | 0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,0,0,0,1,1,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,hepatitis A 22 | 1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,1,0,1,0,0,0,1,0,0,1,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Hepatitis B 23 | 0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Hepatitis C 24 | 0,0,0,0,0,0,1,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Hepatitis D 25 | 0,0,0,0,0,0,1,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,1,1,1,0,0,0,1,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Hepatitis E 26 | 0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Alcoholic hepatitis 27 | 0,0,0,0,0,1,0,0,0,0,0,1,0,0,1,0,0,0,0,1,0,0,0,0,1,1,0,1,1,0,0,0,0,0,0,1,0,0,0,0,0,1,0,1,0,0,0,1,1,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,Tuberculosis 28 | 0,0,0,1,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Common Cold 29 | 0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,1,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Pneumonia 30 | 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Dimorphic hemmorhoids(piles) 31 | 0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Heart attack 32 | 0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,Varicose veins 33 | 0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,1,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Hypothyroidism 34 | 0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,1,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Hyperthyroidism 35 | 0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,Hypoglycemia 36 | 0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,Osteoarthristis 37 | 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,Arthritis 38 | 0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,(vertigo) Paroymsal Positional Vertigo 39 | 0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,Acne 40 | 0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,Urinary tract infection 41 | 0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,0,0,0,Psoriasis 42 | 0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,Impetigo 43 | -------------------------------------------------------------------------------- /inclass.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import matplotlib.pyplot as plt 3 | import pandas as pd 4 | import seaborn as sns 5 | 6 | dataset = pd.read_csv('Life_Expectancy.csv') 7 | 8 | sns.scatterplot(dataset['Adult_Mortality'], dataset['Expected']) 9 | 10 | from scipy.stats import pearsonr 11 | 12 | pearsonr(dataset['Adult_Mortality'], dataset['Expected']) 13 | 14 | X = dataset['Income_Index'] 15 | y = dataset['Expected'] 16 | 17 | import statsmodels.api as sm 18 | 19 | model = sm.OLS(y, sm.add_constant(X)).fit() 20 | print(model.summary()) 21 | 22 | y_pred = model.predict() 23 | 24 | error = y - y_pred 25 | error 26 | 27 | residual = np.sum(error) 28 | 29 | expected = residual / X.shape[0] 30 | expected 31 | 32 | X = dataset.iloc[:, [4, 8, 9]] 33 | 34 | model = sm.OLS(y, sm.add_constant(X)).fit() 35 | print(model.summary()) 36 | 37 | encoded = pd.get_dummies(dataset['Status'], drop_first = True) 38 | 39 | X = dataset.iloc[:, [4, 8, 9]] 40 | X = pd.concat([X, encoded], axis = 1) 41 | 42 | model = sm.OLS(y, sm.add_constant(X)).fit() 43 | print(model.summary()) 44 | 45 | X = dataset.iloc[:, 2:-1] 46 | X = pd.concat([X, encoded], axis = 1) 47 | 48 | model = sm.OLS(y, sm.add_constant(X)).fit() 49 | print(model.summary()) 50 | 51 | p_values = pd.DataFrame(model.pvalues, columns = ['p_values']) 52 | 53 | p_values[p_values['p_values'] < 0.05] 54 | 55 | X = dataset[['GDP', 'Income_Index']] 56 | 57 | model = sm.OLS(y, sm.add_constant(X)).fit() 58 | print(model.summary()) 59 | 60 | sst = np.sum((y - y.mean()) ** 2) 61 | sst 62 | 63 | X = dataset['Income_Index'] 64 | model = sm.OLS(y, X).fit() 65 | print(model.summary()) 66 | 67 | model.conf_int() 68 | 69 | X = dataset.iloc[:, [2, 3, 7]] 70 | 71 | model = sm.OLS(y, sm.add_constant(X)).fit() 72 | print(model.summary()) 73 | 74 | X = dataset.iloc[:, [2, 3, 7, 12]] 75 | 76 | model = sm.OLS(y, sm.add_constant(X)).fit() 77 | print(model.summary()) 78 | 79 | X = dataset.iloc[:, 2:-1] 80 | X = pd.concat([X, encoded], axis = 1) 81 | 82 | model = sm.OLS(y, sm.add_constant(X)).fit() 83 | print(model.summary()) 84 | 85 | np.format_float_positional(model.f_pvalue) 86 | 87 | from statsmodels.graphics.gofplots import qqplot 88 | 89 | qqplot(error, line = 'r') 90 | 91 | X['interaction'] = X['Developing'] * X['GDP'] 92 | 93 | model = sm.OLS(y, sm.add_constant(X)).fit() 94 | print(model.summary()) 95 | 96 | X = X.iloc[:, :-1] 97 | 98 | model = sm.OLS(y, X).fit() 99 | print(model.summary()) 100 | 101 | ################################################# 102 | 103 | dataset = pd.read_csv('LungCapdata.csv') 104 | 105 | pd.plotting.scatter_matrix(dataset) 106 | sns.heatmap(dataset.corr(), annot = True) 107 | 108 | X = dataset.iloc[:, :-1] 109 | 110 | from statsmodels.stats.outliers_influence import variance_inflation_factor 111 | 112 | vif = [variance_inflation_factor(X.values, i) for i in range(X.shape[1])] 113 | 114 | y = dataset.iloc[:, -1] 115 | 116 | model = sm.OLS(y, sm.add_constant(X)).fit() 117 | print(model.summary()) 118 | 119 | y_hat = model.predict() 120 | 121 | residual = y - y_hat 122 | residual 123 | 124 | residuals = model.resid 125 | residuals 126 | 127 | fitted = model.fittedvalues 128 | fitted 129 | 130 | sns.scatterplot(fitted, residuals) 131 | 132 | from scipy.stats import shapiro 133 | shapiro(residuals) 134 | 135 | from sklearn.metrics import mean_squared_error, mean_absolute_error 136 | 137 | np.sqrt(mean_squared_error(y, y_hat)) 138 | mean_absolute_error(y, y_hat) 139 | 140 | def mape(y, y_hat): 141 | return ((np.sum(np.abs(y - y_hat) / y)) / y.shape[0]) * 100 142 | 143 | mape(y, y_hat) 144 | 145 | from sklearn.model_selection import train_test_split 146 | 147 | X_train, X_test, y_train, y_test = train_test_split(X, y) 148 | 149 | from sklearn.linear_model import LinearRegression 150 | lin_reg = LinearRegression() 151 | lin_reg.fit(X_train, y_train) 152 | 153 | lin_reg.score(X_train, y_train) 154 | lin_reg.score(X_test, y_test) 155 | 156 | 157 | 158 | 159 | 160 | 161 | 162 | 163 | 164 | 165 | 166 | 167 | 168 | 169 | 170 | 171 | 172 | 173 | 174 | 175 | 176 | 177 | 178 | 179 | 180 | 181 | 182 | 183 | 184 | 185 | 186 | 187 | 188 | 189 | 190 | 191 | 192 | 193 | 194 | 195 | 196 | 197 | 198 | 199 | 200 | 201 | 202 | 203 | 204 | 205 | 206 | 207 | 208 | 209 | 210 | 211 | 212 | 213 | 214 | 215 | 216 | 217 | 218 | 219 | 220 | 221 | 222 | 223 | 224 | 225 | 226 | 227 | 228 | 229 | 230 | 231 | 232 | 233 | 234 | 235 | 236 | 237 | 238 | 239 | 240 | 241 | 242 | 243 | 244 | 245 | 246 | 247 | 248 | 249 | 250 | 251 | 252 | 253 | 254 | 255 | 256 | 257 | 258 | 259 | 260 | 261 | 262 | 263 | 264 | 265 | 266 | 267 | 268 | 269 | 270 | 271 | 272 | 273 | 274 | 275 | 276 | 277 | 278 | 279 | 280 | 281 | 282 | 283 | 284 | 285 | 286 | 287 | 288 | 289 | 290 | 291 | 292 | 293 | 294 | 295 | 296 | 297 | 298 | 299 | 300 | 301 | 302 | 303 | 304 | 305 | 306 | 307 | 308 | 309 | 310 | 311 | 312 | 313 | 314 | 315 | 316 | 317 | 318 | 319 | 320 | 321 | 322 | 323 | -------------------------------------------------------------------------------- /movie_recommendation.zip: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/tesla-is/ML-Projects/f37256bc3451c6401d7c5a8a028171540957551c/movie_recommendation.zip -------------------------------------------------------------------------------- /tweet.py: -------------------------------------------------------------------------------- 1 | 2 | import sys,tweepy,csv,re 3 | from textblob import TextBlob 4 | import matplotlib.pyplot as plt, mpld3 5 | 6 | 7 | class SentimentAnalysis: 8 | 9 | def __init__(self): 10 | self.tweets = [] 11 | self.tweetText = [] 12 | 13 | def DownloadData(self): 14 | # authenticating 15 | consumerKey = '' 16 | consumerSecret = '' 17 | accessToken = '' 18 | accessTokenSecret = '' 19 | auth = tweepy.OAuthHandler(consumerKey, consumerSecret) 20 | auth.set_access_token(accessToken, accessTokenSecret) 21 | api = tweepy.API(auth) 22 | 23 | # input for term to be searched and how many tweets to search 24 | searchTerm = input("Enter Keyword/Tag to search about: ") 25 | NoOfTerms = int(input("Enter how many tweets to search: ")) 26 | 27 | # searching for tweets 28 | self.tweets = tweepy.Cursor(api.search, q=searchTerm, lang = "en").items(NoOfTerms) 29 | 30 | # Open/create a file to append data to 31 | csvFile = open('result.csv', 'a') 32 | 33 | # Use csv writer 34 | csvWriter = csv.writer(csvFile) 35 | 36 | 37 | # creating some variables to store info 38 | polarity = 0 39 | positive = 0 40 | wpositive = 0 41 | spositive = 0 42 | negative = 0 43 | wnegative = 0 44 | snegative = 0 45 | neutral = 0 46 | 47 | 48 | # iterating through tweets fetched 49 | for tweet in self.tweets: 50 | #Append to temp so that we can store in csv later. I use encode UTF-8 51 | self.tweetText.append(self.cleanTweet(tweet.text).encode('utf-8')) 52 | # print (tweet.text.translate(non_bmp_map)) #print tweet's text 53 | analysis = TextBlob(tweet.text) 54 | # print(analysis.sentiment) # print tweet's polarity 55 | polarity += analysis.sentiment.polarity # adding up polarities to find the average later 56 | 57 | if (analysis.sentiment.polarity == 0): # adding reaction of how people are reacting to find average later 58 | neutral += 1 59 | elif (analysis.sentiment.polarity > 0 and analysis.sentiment.polarity <= 0.3): 60 | wpositive += 1 61 | elif (analysis.sentiment.polarity > 0.3 and analysis.sentiment.polarity <= 0.6): 62 | positive += 1 63 | elif (analysis.sentiment.polarity > 0.6 and analysis.sentiment.polarity <= 1): 64 | spositive += 1 65 | elif (analysis.sentiment.polarity > -0.3 and analysis.sentiment.polarity <= 0): 66 | wnegative += 1 67 | elif (analysis.sentiment.polarity > -0.6 and analysis.sentiment.polarity <= -0.3): 68 | negative += 1 69 | elif (analysis.sentiment.polarity > -1 and analysis.sentiment.polarity <= -0.6): 70 | snegative += 1 71 | 72 | 73 | # Write to csv and close csv file 74 | csvWriter.writerow(self.tweetText) 75 | csvFile.close() 76 | 77 | # finding average of how people are reacting 78 | positive = self.percentage(positive, NoOfTerms) 79 | wpositive = self.percentage(wpositive, NoOfTerms) 80 | spositive = self.percentage(spositive, NoOfTerms) 81 | negative = self.percentage(negative, NoOfTerms) 82 | wnegative = self.percentage(wnegative, NoOfTerms) 83 | snegative = self.percentage(snegative, NoOfTerms) 84 | neutral = self.percentage(neutral, NoOfTerms) 85 | 86 | # finding average reaction 87 | polarity = polarity / NoOfTerms 88 | 89 | # printing out data 90 | print("How people are reacting on " + searchTerm + " by analyzing " + str(NoOfTerms) + " tweets.") 91 | print() 92 | print("General Report: ") 93 | 94 | if (polarity == 0): 95 | print("Neutral") 96 | elif (polarity > 0 and polarity <= 0.3): 97 | print("Weakly Positive") 98 | elif (polarity > 0.3 and polarity <= 0.6): 99 | print("Positive") 100 | elif (polarity > 0.6 and polarity <= 1): 101 | print("Strongly Positive") 102 | elif (polarity > -0.3 and polarity <= 0): 103 | print("Weakly Negative") 104 | elif (polarity > -0.6 and polarity <= -0.3): 105 | print("Negative") 106 | elif (polarity > -1 and polarity <= -0.6): 107 | print("Strongly Negative") 108 | 109 | print() 110 | print("Detailed Report: ") 111 | print(str(positive) + "% people thought it was positive") 112 | print(str(wpositive) + "% people thought it was weakly positive") 113 | print(str(spositive) + "% people thought it was strongly positive") 114 | print(str(negative) + "% people thought it was negative") 115 | print(str(wnegative) + "% people thought it was weakly negative") 116 | print(str(snegative) + "% people thought it was strongly negative") 117 | print(str(neutral) + "% people thought it was neutral") 118 | 119 | self.plotPieChart(positive, wpositive, spositive, negative, wnegative, snegative, neutral, searchTerm, NoOfTerms) 120 | 121 | 122 | def cleanTweet(self, tweet): 123 | # Remove Links, Special Characters etc from tweet 124 | return ' '.join(re.sub("(@[A-Za-z0-9]+)|([^0-9A-Za-z \t]) | (\w +:\ / \ / \S +)", " ", tweet).split()) 125 | 126 | # function to calculate percentage 127 | def percentage(self, part, whole): 128 | temp = 100 * float(part) / float(whole) 129 | return format(temp, '.2f') 130 | 131 | def plotPieChart(self, positive, wpositive, spositive, negative, wnegative, snegative, neutral, searchTerm, noOfSearchTerms): 132 | labels = ['Positive [' + str(positive) + '%]', 'Weakly Positive [' + str(wpositive) + '%]','Strongly Positive [' + str(spositive) + '%]', 'Neutral [' + str(neutral) + '%]', 133 | 'Negative [' + str(negative) + '%]', 'Weakly Negative [' + str(wnegative) + '%]', 'Strongly Negative [' + str(snegative) + '%]'] 134 | sizes = [positive, wpositive, spositive, neutral, negative, wnegative, snegative] 135 | colors = ['yellowgreen','lightgreen','darkgreen', 'gold', 'red','lightsalmon','darkred'] 136 | patches, texts = plt.pie(sizes, colors=colors, startangle=90) 137 | plt.legend(patches, labels, loc="best") 138 | plt.title('How people are reacting on ' + searchTerm + ' by analyzing ' + str(noOfSearchTerms) + ' Tweets.') 139 | plt.axis('equal') 140 | plt.tight_layout() 141 | plt.show() 142 | 143 | 144 | 145 | #plt.plot([3,1,4,1,5], 'ks-', mec='w', mew=5, ms=20) 146 | #mpld3.enable_notebook() 147 | #mpld3.show() 148 | 149 | 150 | 151 | if __name__== "__main__": 152 | sa = SentimentAnalysis() 153 | sa.DownloadData() 154 | 155 | 156 | 157 | 158 | 159 | 160 | 161 | 162 | 163 | 164 | 165 | 166 | 167 | 168 | --------------------------------------------------------------------------------