├── Area and Population ├── Area_and_Population.ipynb └── data │ └── california_cities.csv ├── Bankruptcy Prediction Model with Machine Learning ├── Bankruptcy_Prediction_Model.ipynb └── data │ └── bank.csv ├── Billionaires Analysis with Python ├── Billionaries Analysis with Python.ipynb └── data │ └── Billionaries Analysis.csv ├── Birth Rate Analysis ├── Birth Rate Analysis.ipynb └── data │ └── births.csv ├── Car Price Prediction with Machine Learning ├── Car Price Prediction.ipynb └── data │ └── Car Price Prediction.csv ├── Chatbot using Python └── Chatbot.ipynb ├── Click-Through Rate Prediction with Machine Learning ├── Click-Through Rate Prediction.ipynb ├── data │ └── advertising.csv └── img │ ├── Newspaper.png │ ├── Radio.png │ └── TV.png ├── Count Objects in Image using Python ├── Count_Objects_in_Image.ipynb └── data │ ├── Capture.JPG │ └── cars.webp ├── Covid-19 Deaths Prediction ├── Covid-19 Deaths Prediction.ipynb └── data │ └── COVID_19_Report.csv ├── Covid-19 Vaccines Analysis with Python ├── Covid-19 Vaccines Analysis.ipynb ├── data │ └── country_vaccinations.csv └── final_output.png ├── Data Science Project on President Heights ├── Data Science Project on President Heights.ipynb └── data │ └── heights.csv ├── Data Science Project on Time Series with python using a Dataset- Fremont Bridge Bicycle Counts ├── Time_Series.ipynb └── data │ └── fremont_bridge.csv ├── Dogecoin Price Prediction with Machine Learning ├── Dogecoin_Price_Prediction.ipynb └── data │ └── DOGE-USD.csv ├── Extract Keywords using Python └── Extract_Keywords.ipynb ├── Financial Budget Analysis with Python ├── Financial Budget Analysis.ipynb └── data │ └── india_2021_financial_budget.csv ├── Future Sales Prediction with Machine Learning ├── Future Sales Prediction with Machine Learning.ipynb ├── data │ └── salse.csv └── newplot.png ├── Google Search Analysis with Python ├── Google Search Analysis.ipynb └── data │ └── output.png ├── Instagram Reach Analysis and Prediction ├── Instagram Reach Analysis and Prediction.ipynb ├── data │ └── instadata.csv └── output.png ├── Keyword Research with Python └── Keyword_Research_with_Python.ipynb ├── Language Detection with Python └── Language Detection.ipynb ├── Language Translator using Python └── Language_Translator.ipynb ├── Machine Learning Project Walkthrough with Python ├── Machine Learning Project Walkthrough.ipynb └── data │ ├── gender_submission.csv │ ├── test.csv │ └── train.csv ├── Microsoft Stock Price Prediction with Machine Learning ├── Data │ └── MSFT.csv └── Microsoft Stock Price Prediction.ipynb ├── Online Payments Fraud Detection using Python ├── Online Payments Fraud Detection using Python.ipynb └── newplot.png ├── Profit Prediction with Machine Learning ├── Profit_Prediction.ipynb └── data │ └── Startups.csv ├── README.md ├── Saving a Machine Learning Model ├── Save_Machine_Learning_Model.ipynb ├── data │ └── student_mat.csv └── pickle_model ├── Spam_Detection_with_Machine_Learning ├── Spam Detection.ipynb └── data │ └── spam_messege.csv ├── Student Grades Prediction with Machine Learning ├── Student Grades Prediction.ipynb └── data │ └── student_mat.csv ├── Text Emotions Detection with Machine Learning ├── Text_Emotions_Detection_with_Machine_Learning.ipynb └── data │ └── text.txt ├── Text Summarization with Python ├── Text_Summarization.ipynb └── img.webp ├── Uber Trips Analysis using Python ├── Uber Trips Analysis using Python.ipynb └── data │ └── Documentation.txt ├── Ukraine Russia War Twitter Sentiment Analysis using Python ├── Ukraine Russia War Twitter Sentiment Analysis.ipynb ├── data │ └── filename.csv └── output.png ├── Unemployment_Analysis_with_Python ├── Unemployment_Analysis_with_Python.ipynb ├── data │ └── Unemployment_Analysis_data.csv └── newplot.png ├── Video Game Sales Prediction Model with Python ├── Video Game Sales Prediction.ipynb └── data │ └── vgsales.csv ├── Waiter Tips Prediction with Machine Learning ├── Waiter Tips Prediction with Machine Learning.ipynb ├── data │ └── waiters tips data.csv └── newplot.png ├── WhatsApp Chat Sentiment Analysis using Python ├── WhatsApp_Chat_Sentiment_Analysis.ipynb └── data │ └── WhatsApp Chat with HRV MLDS Community.txt └── student_project ├── Dataset.xlsx └── rice_type_all_algo.ipynb /Car Price Prediction with Machine Learning/data/Car Price Prediction.csv: -------------------------------------------------------------------------------- 1 | car_ID,symboling,CarName,fueltype,aspiration,doornumber,carbody,drivewheel,enginelocation,wheelbase,carlength,carwidth,carheight,curbweight,enginetype,cylindernumber,enginesize,fuelsystem,boreratio,stroke,compressionratio,horsepower,peakrpm,citympg,highwaympg,price 2 | 1,3,alfa-romero giulia,gas,std,two,convertible,rwd,front,88.6,168.8,64.1,48.8,2548,dohc,four,130,mpfi,3.47,2.68,9,111,5000,21,27,13495 3 | 2,3,alfa-romero stelvio,gas,std,two,convertible,rwd,front,88.6,168.8,64.1,48.8,2548,dohc,four,130,mpfi,3.47,2.68,9,111,5000,21,27,16500 4 | 3,1,alfa-romero Quadrifoglio,gas,std,two,hatchback,rwd,front,94.5,171.2,65.5,52.4,2823,ohcv,six,152,mpfi,2.68,3.47,9,154,5000,19,26,16500 5 | 4,2,audi 100 ls,gas,std,four,sedan,fwd,front,99.8,176.6,66.2,54.3,2337,ohc,four,109,mpfi,3.19,3.4,10,102,5500,24,30,13950 6 | 5,2,audi 100ls,gas,std,four,sedan,4wd,front,99.4,176.6,66.4,54.3,2824,ohc,five,136,mpfi,3.19,3.4,8,115,5500,18,22,17450 7 | 6,2,audi fox,gas,std,two,sedan,fwd,front,99.8,177.3,66.3,53.1,2507,ohc,five,136,mpfi,3.19,3.4,8.5,110,5500,19,25,15250 8 | 7,1,audi 100ls,gas,std,four,sedan,fwd,front,105.8,192.7,71.4,55.7,2844,ohc,five,136,mpfi,3.19,3.4,8.5,110,5500,19,25,17710 9 | 8,1,audi 5000,gas,std,four,wagon,fwd,front,105.8,192.7,71.4,55.7,2954,ohc,five,136,mpfi,3.19,3.4,8.5,110,5500,19,25,18920 10 | 9,1,audi 4000,gas,turbo,four,sedan,fwd,front,105.8,192.7,71.4,55.9,3086,ohc,five,131,mpfi,3.13,3.4,8.3,140,5500,17,20,23875 11 | 10,0,audi 5000s (diesel),gas,turbo,two,hatchback,4wd,front,99.5,178.2,67.9,52,3053,ohc,five,131,mpfi,3.13,3.4,7,160,5500,16,22,17859.167 12 | 11,2,bmw 320i,gas,std,two,sedan,rwd,front,101.2,176.8,64.8,54.3,2395,ohc,four,108,mpfi,3.5,2.8,8.8,101,5800,23,29,16430 13 | 12,0,bmw 320i,gas,std,four,sedan,rwd,front,101.2,176.8,64.8,54.3,2395,ohc,four,108,mpfi,3.5,2.8,8.8,101,5800,23,29,16925 14 | 13,0,bmw x1,gas,std,two,sedan,rwd,front,101.2,176.8,64.8,54.3,2710,ohc,six,164,mpfi,3.31,3.19,9,121,4250,21,28,20970 15 | 14,0,bmw x3,gas,std,four,sedan,rwd,front,101.2,176.8,64.8,54.3,2765,ohc,six,164,mpfi,3.31,3.19,9,121,4250,21,28,21105 16 | 15,1,bmw z4,gas,std,four,sedan,rwd,front,103.5,189,66.9,55.7,3055,ohc,six,164,mpfi,3.31,3.19,9,121,4250,20,25,24565 17 | 16,0,bmw x4,gas,std,four,sedan,rwd,front,103.5,189,66.9,55.7,3230,ohc,six,209,mpfi,3.62,3.39,8,182,5400,16,22,30760 18 | 17,0,bmw x5,gas,std,two,sedan,rwd,front,103.5,193.8,67.9,53.7,3380,ohc,six,209,mpfi,3.62,3.39,8,182,5400,16,22,41315 19 | 18,0,bmw x3,gas,std,four,sedan,rwd,front,110,197,70.9,56.3,3505,ohc,six,209,mpfi,3.62,3.39,8,182,5400,15,20,36880 20 | 19,2,chevrolet impala,gas,std,two,hatchback,fwd,front,88.4,141.1,60.3,53.2,1488,l,three,61,2bbl,2.91,3.03,9.5,48,5100,47,53,5151 21 | 20,1,chevrolet monte carlo,gas,std,two,hatchback,fwd,front,94.5,155.9,63.6,52,1874,ohc,four,90,2bbl,3.03,3.11,9.6,70,5400,38,43,6295 22 | 21,0,chevrolet vega 2300,gas,std,four,sedan,fwd,front,94.5,158.8,63.6,52,1909,ohc,four,90,2bbl,3.03,3.11,9.6,70,5400,38,43,6575 23 | 22,1,dodge rampage,gas,std,two,hatchback,fwd,front,93.7,157.3,63.8,50.8,1876,ohc,four,90,2bbl,2.97,3.23,9.41,68,5500,37,41,5572 24 | 23,1,dodge challenger se,gas,std,two,hatchback,fwd,front,93.7,157.3,63.8,50.8,1876,ohc,four,90,2bbl,2.97,3.23,9.4,68,5500,31,38,6377 25 | 24,1,dodge d200,gas,turbo,two,hatchback,fwd,front,93.7,157.3,63.8,50.8,2128,ohc,four,98,mpfi,3.03,3.39,7.6,102,5500,24,30,7957 26 | 25,1,dodge monaco (sw),gas,std,four,hatchback,fwd,front,93.7,157.3,63.8,50.6,1967,ohc,four,90,2bbl,2.97,3.23,9.4,68,5500,31,38,6229 27 | 26,1,dodge colt hardtop,gas,std,four,sedan,fwd,front,93.7,157.3,63.8,50.6,1989,ohc,four,90,2bbl,2.97,3.23,9.4,68,5500,31,38,6692 28 | 27,1,dodge colt (sw),gas,std,four,sedan,fwd,front,93.7,157.3,63.8,50.6,1989,ohc,four,90,2bbl,2.97,3.23,9.4,68,5500,31,38,7609 29 | 28,1,dodge coronet custom,gas,turbo,two,sedan,fwd,front,93.7,157.3,63.8,50.6,2191,ohc,four,98,mpfi,3.03,3.39,7.6,102,5500,24,30,8558 30 | 29,-1,dodge dart custom,gas,std,four,wagon,fwd,front,103.3,174.6,64.6,59.8,2535,ohc,four,122,2bbl,3.34,3.46,8.5,88,5000,24,30,8921 31 | 30,3,dodge coronet custom (sw),gas,turbo,two,hatchback,fwd,front,95.9,173.2,66.3,50.2,2811,ohc,four,156,mfi,3.6,3.9,7,145,5000,19,24,12964 32 | 31,2,honda civic,gas,std,two,hatchback,fwd,front,86.6,144.6,63.9,50.8,1713,ohc,four,92,1bbl,2.91,3.41,9.6,58,4800,49,54,6479 33 | 32,2,honda civic cvcc,gas,std,two,hatchback,fwd,front,86.6,144.6,63.9,50.8,1819,ohc,four,92,1bbl,2.91,3.41,9.2,76,6000,31,38,6855 34 | 33,1,honda civic,gas,std,two,hatchback,fwd,front,93.7,150,64,52.6,1837,ohc,four,79,1bbl,2.91,3.07,10.1,60,5500,38,42,5399 35 | 34,1,honda accord cvcc,gas,std,two,hatchback,fwd,front,93.7,150,64,52.6,1940,ohc,four,92,1bbl,2.91,3.41,9.2,76,6000,30,34,6529 36 | 35,1,honda civic cvcc,gas,std,two,hatchback,fwd,front,93.7,150,64,52.6,1956,ohc,four,92,1bbl,2.91,3.41,9.2,76,6000,30,34,7129 37 | 36,0,honda accord lx,gas,std,four,sedan,fwd,front,96.5,163.4,64,54.5,2010,ohc,four,92,1bbl,2.91,3.41,9.2,76,6000,30,34,7295 38 | 37,0,honda civic 1500 gl,gas,std,four,wagon,fwd,front,96.5,157.1,63.9,58.3,2024,ohc,four,92,1bbl,2.92,3.41,9.2,76,6000,30,34,7295 39 | 38,0,honda accord,gas,std,two,hatchback,fwd,front,96.5,167.5,65.2,53.3,2236,ohc,four,110,1bbl,3.15,3.58,9,86,5800,27,33,7895 40 | 39,0,honda civic 1300,gas,std,two,hatchback,fwd,front,96.5,167.5,65.2,53.3,2289,ohc,four,110,1bbl,3.15,3.58,9,86,5800,27,33,9095 41 | 40,0,honda prelude,gas,std,four,sedan,fwd,front,96.5,175.4,65.2,54.1,2304,ohc,four,110,1bbl,3.15,3.58,9,86,5800,27,33,8845 42 | 41,0,honda accord,gas,std,four,sedan,fwd,front,96.5,175.4,62.5,54.1,2372,ohc,four,110,1bbl,3.15,3.58,9,86,5800,27,33,10295 43 | 42,0,honda civic,gas,std,four,sedan,fwd,front,96.5,175.4,65.2,54.1,2465,ohc,four,110,mpfi,3.15,3.58,9,101,5800,24,28,12945 44 | 43,1,honda civic (auto),gas,std,two,sedan,fwd,front,96.5,169.1,66,51,2293,ohc,four,110,2bbl,3.15,3.58,9.1,100,5500,25,31,10345 45 | 44,0,isuzu MU-X,gas,std,four,sedan,rwd,front,94.3,170.7,61.8,53.5,2337,ohc,four,111,2bbl,3.31,3.23,8.5,78,4800,24,29,6785 46 | 45,1,isuzu D-Max ,gas,std,two,sedan,fwd,front,94.5,155.9,63.6,52,1874,ohc,four,90,2bbl,3.03,3.11,9.6,70,5400,38,43,8916.5 47 | 46,0,isuzu D-Max V-Cross,gas,std,four,sedan,fwd,front,94.5,155.9,63.6,52,1909,ohc,four,90,2bbl,3.03,3.11,9.6,70,5400,38,43,8916.5 48 | 47,2,isuzu D-Max ,gas,std,two,hatchback,rwd,front,96,172.6,65.2,51.4,2734,ohc,four,119,spfi,3.43,3.23,9.2,90,5000,24,29,11048 49 | 48,0,jaguar xj,gas,std,four,sedan,rwd,front,113,199.6,69.6,52.8,4066,dohc,six,258,mpfi,3.63,4.17,8.1,176,4750,15,19,32250 50 | 49,0,jaguar xf,gas,std,four,sedan,rwd,front,113,199.6,69.6,52.8,4066,dohc,six,258,mpfi,3.63,4.17,8.1,176,4750,15,19,35550 51 | 50,0,jaguar xk,gas,std,two,sedan,rwd,front,102,191.7,70.6,47.8,3950,ohcv,twelve,326,mpfi,3.54,2.76,11.5,262,5000,13,17,36000 52 | 51,1,maxda rx3,gas,std,two,hatchback,fwd,front,93.1,159.1,64.2,54.1,1890,ohc,four,91,2bbl,3.03,3.15,9,68,5000,30,31,5195 53 | 52,1,maxda glc deluxe,gas,std,two,hatchback,fwd,front,93.1,159.1,64.2,54.1,1900,ohc,four,91,2bbl,3.03,3.15,9,68,5000,31,38,6095 54 | 53,1,mazda rx2 coupe,gas,std,two,hatchback,fwd,front,93.1,159.1,64.2,54.1,1905,ohc,four,91,2bbl,3.03,3.15,9,68,5000,31,38,6795 55 | 54,1,mazda rx-4,gas,std,four,sedan,fwd,front,93.1,166.8,64.2,54.1,1945,ohc,four,91,2bbl,3.03,3.15,9,68,5000,31,38,6695 56 | 55,1,mazda glc deluxe,gas,std,four,sedan,fwd,front,93.1,166.8,64.2,54.1,1950,ohc,four,91,2bbl,3.08,3.15,9,68,5000,31,38,7395 57 | 56,3,mazda 626,gas,std,two,hatchback,rwd,front,95.3,169,65.7,49.6,2380,rotor,two,70,4bbl,3.33,3.255,9.4,101,6000,17,23,10945 58 | 57,3,mazda glc,gas,std,two,hatchback,rwd,front,95.3,169,65.7,49.6,2380,rotor,two,70,4bbl,3.33,3.255,9.4,101,6000,17,23,11845 59 | 58,3,mazda rx-7 gs,gas,std,two,hatchback,rwd,front,95.3,169,65.7,49.6,2385,rotor,two,70,4bbl,3.33,3.255,9.4,101,6000,17,23,13645 60 | 59,3,mazda glc 4,gas,std,two,hatchback,rwd,front,95.3,169,65.7,49.6,2500,rotor,two,80,mpfi,3.33,3.255,9.4,135,6000,16,23,15645 61 | 60,1,mazda 626,gas,std,two,hatchback,fwd,front,98.8,177.8,66.5,53.7,2385,ohc,four,122,2bbl,3.39,3.39,8.6,84,4800,26,32,8845 62 | 61,0,mazda glc custom l,gas,std,four,sedan,fwd,front,98.8,177.8,66.5,55.5,2410,ohc,four,122,2bbl,3.39,3.39,8.6,84,4800,26,32,8495 63 | 62,1,mazda glc custom,gas,std,two,hatchback,fwd,front,98.8,177.8,66.5,53.7,2385,ohc,four,122,2bbl,3.39,3.39,8.6,84,4800,26,32,10595 64 | 63,0,mazda rx-4,gas,std,four,sedan,fwd,front,98.8,177.8,66.5,55.5,2410,ohc,four,122,2bbl,3.39,3.39,8.6,84,4800,26,32,10245 65 | 64,0,mazda glc deluxe,diesel,std,four,sedan,fwd,front,98.8,177.8,66.5,55.5,2443,ohc,four,122,idi,3.39,3.39,22.7,64,4650,36,42,10795 66 | 65,0,mazda 626,gas,std,four,hatchback,fwd,front,98.8,177.8,66.5,55.5,2425,ohc,four,122,2bbl,3.39,3.39,8.6,84,4800,26,32,11245 67 | 66,0,mazda glc,gas,std,four,sedan,rwd,front,104.9,175,66.1,54.4,2670,ohc,four,140,mpfi,3.76,3.16,8,120,5000,19,27,18280 68 | 67,0,mazda rx-7 gs,diesel,std,four,sedan,rwd,front,104.9,175,66.1,54.4,2700,ohc,four,134,idi,3.43,3.64,22,72,4200,31,39,18344 69 | 68,-1,buick electra 225 custom,diesel,turbo,four,sedan,rwd,front,110,190.9,70.3,56.5,3515,ohc,five,183,idi,3.58,3.64,21.5,123,4350,22,25,25552 70 | 69,-1,buick century luxus (sw),diesel,turbo,four,wagon,rwd,front,110,190.9,70.3,58.7,3750,ohc,five,183,idi,3.58,3.64,21.5,123,4350,22,25,28248 71 | 70,0,buick century,diesel,turbo,two,hardtop,rwd,front,106.7,187.5,70.3,54.9,3495,ohc,five,183,idi,3.58,3.64,21.5,123,4350,22,25,28176 72 | 71,-1,buick skyhawk,diesel,turbo,four,sedan,rwd,front,115.6,202.6,71.7,56.3,3770,ohc,five,183,idi,3.58,3.64,21.5,123,4350,22,25,31600 73 | 72,-1,buick opel isuzu deluxe,gas,std,four,sedan,rwd,front,115.6,202.6,71.7,56.5,3740,ohcv,eight,234,mpfi,3.46,3.1,8.3,155,4750,16,18,34184 74 | 73,3,buick skylark,gas,std,two,convertible,rwd,front,96.6,180.3,70.5,50.8,3685,ohcv,eight,234,mpfi,3.46,3.1,8.3,155,4750,16,18,35056 75 | 74,0,buick century special,gas,std,four,sedan,rwd,front,120.9,208.1,71.7,56.7,3900,ohcv,eight,308,mpfi,3.8,3.35,8,184,4500,14,16,40960 76 | 75,1,buick regal sport coupe (turbo),gas,std,two,hardtop,rwd,front,112,199.2,72,55.4,3715,ohcv,eight,304,mpfi,3.8,3.35,8,184,4500,14,16,45400 77 | 76,1,mercury cougar,gas,turbo,two,hatchback,rwd,front,102.7,178.4,68,54.8,2910,ohc,four,140,mpfi,3.78,3.12,8,175,5000,19,24,16503 78 | 77,2,mitsubishi mirage,gas,std,two,hatchback,fwd,front,93.7,157.3,64.4,50.8,1918,ohc,four,92,2bbl,2.97,3.23,9.4,68,5500,37,41,5389 79 | 78,2,mitsubishi lancer,gas,std,two,hatchback,fwd,front,93.7,157.3,64.4,50.8,1944,ohc,four,92,2bbl,2.97,3.23,9.4,68,5500,31,38,6189 80 | 79,2,mitsubishi outlander,gas,std,two,hatchback,fwd,front,93.7,157.3,64.4,50.8,2004,ohc,four,92,2bbl,2.97,3.23,9.4,68,5500,31,38,6669 81 | 80,1,mitsubishi g4,gas,turbo,two,hatchback,fwd,front,93,157.3,63.8,50.8,2145,ohc,four,98,spdi,3.03,3.39,7.6,102,5500,24,30,7689 82 | 81,3,mitsubishi mirage g4,gas,turbo,two,hatchback,fwd,front,96.3,173,65.4,49.4,2370,ohc,four,110,spdi,3.17,3.46,7.5,116,5500,23,30,9959 83 | 82,3,mitsubishi g4,gas,std,two,hatchback,fwd,front,96.3,173,65.4,49.4,2328,ohc,four,122,2bbl,3.35,3.46,8.5,88,5000,25,32,8499 84 | 83,3,mitsubishi outlander,gas,turbo,two,hatchback,fwd,front,95.9,173.2,66.3,50.2,2833,ohc,four,156,spdi,3.58,3.86,7,145,5000,19,24,12629 85 | 84,3,mitsubishi g4,gas,turbo,two,hatchback,fwd,front,95.9,173.2,66.3,50.2,2921,ohc,four,156,spdi,3.59,3.86,7,145,5000,19,24,14869 86 | 85,3,mitsubishi mirage g4,gas,turbo,two,hatchback,fwd,front,95.9,173.2,66.3,50.2,2926,ohc,four,156,spdi,3.59,3.86,7,145,5000,19,24,14489 87 | 86,1,mitsubishi montero,gas,std,four,sedan,fwd,front,96.3,172.4,65.4,51.6,2365,ohc,four,122,2bbl,3.35,3.46,8.5,88,5000,25,32,6989 88 | 87,1,mitsubishi pajero,gas,std,four,sedan,fwd,front,96.3,172.4,65.4,51.6,2405,ohc,four,122,2bbl,3.35,3.46,8.5,88,5000,25,32,8189 89 | 88,1,mitsubishi outlander,gas,turbo,four,sedan,fwd,front,96.3,172.4,65.4,51.6,2403,ohc,four,110,spdi,3.17,3.46,7.5,116,5500,23,30,9279 90 | 89,-1,mitsubishi mirage g4,gas,std,four,sedan,fwd,front,96.3,172.4,65.4,51.6,2403,ohc,four,110,spdi,3.17,3.46,7.5,116,5500,23,30,9279 91 | 90,1,Nissan versa,gas,std,two,sedan,fwd,front,94.5,165.3,63.8,54.5,1889,ohc,four,97,2bbl,3.15,3.29,9.4,69,5200,31,37,5499 92 | 91,1,nissan gt-r,diesel,std,two,sedan,fwd,front,94.5,165.3,63.8,54.5,2017,ohc,four,103,idi,2.99,3.47,21.9,55,4800,45,50,7099 93 | 92,1,nissan rogue,gas,std,two,sedan,fwd,front,94.5,165.3,63.8,54.5,1918,ohc,four,97,2bbl,3.15,3.29,9.4,69,5200,31,37,6649 94 | 93,1,nissan latio,gas,std,four,sedan,fwd,front,94.5,165.3,63.8,54.5,1938,ohc,four,97,2bbl,3.15,3.29,9.4,69,5200,31,37,6849 95 | 94,1,nissan titan,gas,std,four,wagon,fwd,front,94.5,170.2,63.8,53.5,2024,ohc,four,97,2bbl,3.15,3.29,9.4,69,5200,31,37,7349 96 | 95,1,nissan leaf,gas,std,two,sedan,fwd,front,94.5,165.3,63.8,54.5,1951,ohc,four,97,2bbl,3.15,3.29,9.4,69,5200,31,37,7299 97 | 96,1,nissan juke,gas,std,two,hatchback,fwd,front,94.5,165.6,63.8,53.3,2028,ohc,four,97,2bbl,3.15,3.29,9.4,69,5200,31,37,7799 98 | 97,1,nissan latio,gas,std,four,sedan,fwd,front,94.5,165.3,63.8,54.5,1971,ohc,four,97,2bbl,3.15,3.29,9.4,69,5200,31,37,7499 99 | 98,1,nissan note,gas,std,four,wagon,fwd,front,94.5,170.2,63.8,53.5,2037,ohc,four,97,2bbl,3.15,3.29,9.4,69,5200,31,37,7999 100 | 99,2,nissan clipper,gas,std,two,hardtop,fwd,front,95.1,162.4,63.8,53.3,2008,ohc,four,97,2bbl,3.15,3.29,9.4,69,5200,31,37,8249 101 | 100,0,nissan rogue,gas,std,four,hatchback,fwd,front,97.2,173.4,65.2,54.7,2324,ohc,four,120,2bbl,3.33,3.47,8.5,97,5200,27,34,8949 102 | 101,0,nissan nv200,gas,std,four,sedan,fwd,front,97.2,173.4,65.2,54.7,2302,ohc,four,120,2bbl,3.33,3.47,8.5,97,5200,27,34,9549 103 | 102,0,nissan dayz,gas,std,four,sedan,fwd,front,100.4,181.7,66.5,55.1,3095,ohcv,six,181,mpfi,3.43,3.27,9,152,5200,17,22,13499 104 | 103,0,nissan fuga,gas,std,four,wagon,fwd,front,100.4,184.6,66.5,56.1,3296,ohcv,six,181,mpfi,3.43,3.27,9,152,5200,17,22,14399 105 | 104,0,nissan otti,gas,std,four,sedan,fwd,front,100.4,184.6,66.5,55.1,3060,ohcv,six,181,mpfi,3.43,3.27,9,152,5200,19,25,13499 106 | 105,3,nissan teana,gas,std,two,hatchback,rwd,front,91.3,170.7,67.9,49.7,3071,ohcv,six,181,mpfi,3.43,3.27,9,160,5200,19,25,17199 107 | 106,3,nissan kicks,gas,turbo,two,hatchback,rwd,front,91.3,170.7,67.9,49.7,3139,ohcv,six,181,mpfi,3.43,3.27,7.8,200,5200,17,23,19699 108 | 107,1,nissan clipper,gas,std,two,hatchback,rwd,front,99.2,178.5,67.9,49.7,3139,ohcv,six,181,mpfi,3.43,3.27,9,160,5200,19,25,18399 109 | 108,0,peugeot 504,gas,std,four,sedan,rwd,front,107.9,186.7,68.4,56.7,3020,l,four,120,mpfi,3.46,3.19,8.4,97,5000,19,24,11900 110 | 109,0,peugeot 304,diesel,turbo,four,sedan,rwd,front,107.9,186.7,68.4,56.7,3197,l,four,152,idi,3.7,3.52,21,95,4150,28,33,13200 111 | 110,0,peugeot 504 (sw),gas,std,four,wagon,rwd,front,114.2,198.9,68.4,58.7,3230,l,four,120,mpfi,3.46,3.19,8.4,97,5000,19,24,12440 112 | 111,0,peugeot 504,diesel,turbo,four,wagon,rwd,front,114.2,198.9,68.4,58.7,3430,l,four,152,idi,3.7,3.52,21,95,4150,25,25,13860 113 | 112,0,peugeot 504,gas,std,four,sedan,rwd,front,107.9,186.7,68.4,56.7,3075,l,four,120,mpfi,3.46,2.19,8.4,95,5000,19,24,15580 114 | 113,0,peugeot 604sl,diesel,turbo,four,sedan,rwd,front,107.9,186.7,68.4,56.7,3252,l,four,152,idi,3.7,3.52,21,95,4150,28,33,16900 115 | 114,0,peugeot 504,gas,std,four,wagon,rwd,front,114.2,198.9,68.4,56.7,3285,l,four,120,mpfi,3.46,2.19,8.4,95,5000,19,24,16695 116 | 115,0,peugeot 505s turbo diesel,diesel,turbo,four,wagon,rwd,front,114.2,198.9,68.4,58.7,3485,l,four,152,idi,3.7,3.52,21,95,4150,25,25,17075 117 | 116,0,peugeot 504,gas,std,four,sedan,rwd,front,107.9,186.7,68.4,56.7,3075,l,four,120,mpfi,3.46,3.19,8.4,97,5000,19,24,16630 118 | 117,0,peugeot 504,diesel,turbo,four,sedan,rwd,front,107.9,186.7,68.4,56.7,3252,l,four,152,idi,3.7,3.52,21,95,4150,28,33,17950 119 | 118,0,peugeot 604sl,gas,turbo,four,sedan,rwd,front,108,186.7,68.3,56,3130,l,four,134,mpfi,3.61,3.21,7,142,5600,18,24,18150 120 | 119,1,plymouth fury iii,gas,std,two,hatchback,fwd,front,93.7,157.3,63.8,50.8,1918,ohc,four,90,2bbl,2.97,3.23,9.4,68,5500,37,41,5572 121 | 120,1,plymouth cricket,gas,turbo,two,hatchback,fwd,front,93.7,157.3,63.8,50.8,2128,ohc,four,98,spdi,3.03,3.39,7.6,102,5500,24,30,7957 122 | 121,1,plymouth fury iii,gas,std,four,hatchback,fwd,front,93.7,157.3,63.8,50.6,1967,ohc,four,90,2bbl,2.97,3.23,9.4,68,5500,31,38,6229 123 | 122,1,plymouth satellite custom (sw),gas,std,four,sedan,fwd,front,93.7,167.3,63.8,50.8,1989,ohc,four,90,2bbl,2.97,3.23,9.4,68,5500,31,38,6692 124 | 123,1,plymouth fury gran sedan,gas,std,four,sedan,fwd,front,93.7,167.3,63.8,50.8,2191,ohc,four,98,2bbl,2.97,3.23,9.4,68,5500,31,38,7609 125 | 124,-1,plymouth valiant,gas,std,four,wagon,fwd,front,103.3,174.6,64.6,59.8,2535,ohc,four,122,2bbl,3.35,3.46,8.5,88,5000,24,30,8921 126 | 125,3,plymouth duster,gas,turbo,two,hatchback,rwd,front,95.9,173.2,66.3,50.2,2818,ohc,four,156,spdi,3.59,3.86,7,145,5000,19,24,12764 127 | 126,3,porsche macan,gas,std,two,hatchback,rwd,front,94.5,168.9,68.3,50.2,2778,ohc,four,151,mpfi,3.94,3.11,9.5,143,5500,19,27,22018 128 | 127,3,porcshce panamera,gas,std,two,hardtop,rwd,rear,89.5,168.9,65,51.6,2756,ohcf,six,194,mpfi,3.74,2.9,9.5,207,5900,17,25,32528 129 | 128,3,porsche cayenne,gas,std,two,hardtop,rwd,rear,89.5,168.9,65,51.6,2756,ohcf,six,194,mpfi,3.74,2.9,9.5,207,5900,17,25,34028 130 | 129,3,porsche boxter,gas,std,two,convertible,rwd,rear,89.5,168.9,65,51.6,2800,ohcf,six,194,mpfi,3.74,2.9,9.5,207,5900,17,25,37028 131 | 130,1,porsche cayenne,gas,std,two,hatchback,rwd,front,98.4,175.7,72.3,50.5,3366,dohcv,eight,203,mpfi,3.94,3.11,10,288,5750,17,28,31400.5 132 | 131,0,renault 12tl,gas,std,four,wagon,fwd,front,96.1,181.5,66.5,55.2,2579,ohc,four,132,mpfi,3.46,3.9,8.7,90,5100,23,31,9295 133 | 132,2,renault 5 gtl,gas,std,two,hatchback,fwd,front,96.1,176.8,66.6,50.5,2460,ohc,four,132,mpfi,3.46,3.9,8.7,90,5100,23,31,9895 134 | 133,3,saab 99e,gas,std,two,hatchback,fwd,front,99.1,186.6,66.5,56.1,2658,ohc,four,121,mpfi,3.54,3.07,9.31,110,5250,21,28,11850 135 | 134,2,saab 99le,gas,std,four,sedan,fwd,front,99.1,186.6,66.5,56.1,2695,ohc,four,121,mpfi,3.54,3.07,9.3,110,5250,21,28,12170 136 | 135,3,saab 99le,gas,std,two,hatchback,fwd,front,99.1,186.6,66.5,56.1,2707,ohc,four,121,mpfi,2.54,2.07,9.3,110,5250,21,28,15040 137 | 136,2,saab 99gle,gas,std,four,sedan,fwd,front,99.1,186.6,66.5,56.1,2758,ohc,four,121,mpfi,3.54,3.07,9.3,110,5250,21,28,15510 138 | 137,3,saab 99gle,gas,turbo,two,hatchback,fwd,front,99.1,186.6,66.5,56.1,2808,dohc,four,121,mpfi,3.54,3.07,9,160,5500,19,26,18150 139 | 138,2,saab 99e,gas,turbo,four,sedan,fwd,front,99.1,186.6,66.5,56.1,2847,dohc,four,121,mpfi,3.54,3.07,9,160,5500,19,26,18620 140 | 139,2,subaru,gas,std,two,hatchback,fwd,front,93.7,156.9,63.4,53.7,2050,ohcf,four,97,2bbl,3.62,2.36,9,69,4900,31,36,5118 141 | 140,2,subaru dl,gas,std,two,hatchback,fwd,front,93.7,157.9,63.6,53.7,2120,ohcf,four,108,2bbl,3.62,2.64,8.7,73,4400,26,31,7053 142 | 141,2,subaru dl,gas,std,two,hatchback,4wd,front,93.3,157.3,63.8,55.7,2240,ohcf,four,108,2bbl,3.62,2.64,8.7,73,4400,26,31,7603 143 | 142,0,subaru,gas,std,four,sedan,fwd,front,97.2,172,65.4,52.5,2145,ohcf,four,108,2bbl,3.62,2.64,9.5,82,4800,32,37,7126 144 | 143,0,subaru brz,gas,std,four,sedan,fwd,front,97.2,172,65.4,52.5,2190,ohcf,four,108,2bbl,3.62,2.64,9.5,82,4400,28,33,7775 145 | 144,0,subaru baja,gas,std,four,sedan,fwd,front,97.2,172,65.4,52.5,2340,ohcf,four,108,mpfi,3.62,2.64,9,94,5200,26,32,9960 146 | 145,0,subaru r1,gas,std,four,sedan,4wd,front,97,172,65.4,54.3,2385,ohcf,four,108,2bbl,3.62,2.64,9,82,4800,24,25,9233 147 | 146,0,subaru r2,gas,turbo,four,sedan,4wd,front,97,172,65.4,54.3,2510,ohcf,four,108,mpfi,3.62,2.64,7.7,111,4800,24,29,11259 148 | 147,0,subaru trezia,gas,std,four,wagon,fwd,front,97,173.5,65.4,53,2290,ohcf,four,108,2bbl,3.62,2.64,9,82,4800,28,32,7463 149 | 148,0,subaru tribeca,gas,std,four,wagon,fwd,front,97,173.5,65.4,53,2455,ohcf,four,108,mpfi,3.62,2.64,9,94,5200,25,31,10198 150 | 149,0,subaru dl,gas,std,four,wagon,4wd,front,96.9,173.6,65.4,54.9,2420,ohcf,four,108,2bbl,3.62,2.64,9,82,4800,23,29,8013 151 | 150,0,subaru dl,gas,turbo,four,wagon,4wd,front,96.9,173.6,65.4,54.9,2650,ohcf,four,108,mpfi,3.62,2.64,7.7,111,4800,23,23,11694 152 | 151,1,toyota corona mark ii,gas,std,two,hatchback,fwd,front,95.7,158.7,63.6,54.5,1985,ohc,four,92,2bbl,3.05,3.03,9,62,4800,35,39,5348 153 | 152,1,toyota corona,gas,std,two,hatchback,fwd,front,95.7,158.7,63.6,54.5,2040,ohc,four,92,2bbl,3.05,3.03,9,62,4800,31,38,6338 154 | 153,1,toyota corolla 1200,gas,std,four,hatchback,fwd,front,95.7,158.7,63.6,54.5,2015,ohc,four,92,2bbl,3.05,3.03,9,62,4800,31,38,6488 155 | 154,0,toyota corona hardtop,gas,std,four,wagon,fwd,front,95.7,169.7,63.6,59.1,2280,ohc,four,92,2bbl,3.05,3.03,9,62,4800,31,37,6918 156 | 155,0,toyota corolla 1600 (sw),gas,std,four,wagon,4wd,front,95.7,169.7,63.6,59.1,2290,ohc,four,92,2bbl,3.05,3.03,9,62,4800,27,32,7898 157 | 156,0,toyota carina,gas,std,four,wagon,4wd,front,95.7,169.7,63.6,59.1,3110,ohc,four,92,2bbl,3.05,3.03,9,62,4800,27,32,8778 158 | 157,0,toyota mark ii,gas,std,four,sedan,fwd,front,95.7,166.3,64.4,53,2081,ohc,four,98,2bbl,3.19,3.03,9,70,4800,30,37,6938 159 | 158,0,toyota corolla 1200,gas,std,four,hatchback,fwd,front,95.7,166.3,64.4,52.8,2109,ohc,four,98,2bbl,3.19,3.03,9,70,4800,30,37,7198 160 | 159,0,toyota corona,diesel,std,four,sedan,fwd,front,95.7,166.3,64.4,53,2275,ohc,four,110,idi,3.27,3.35,22.5,56,4500,34,36,7898 161 | 160,0,toyota corolla,diesel,std,four,hatchback,fwd,front,95.7,166.3,64.4,52.8,2275,ohc,four,110,idi,3.27,3.35,22.5,56,4500,38,47,7788 162 | 161,0,toyota corona,gas,std,four,sedan,fwd,front,95.7,166.3,64.4,53,2094,ohc,four,98,2bbl,3.19,3.03,9,70,4800,38,47,7738 163 | 162,0,toyota corolla,gas,std,four,hatchback,fwd,front,95.7,166.3,64.4,52.8,2122,ohc,four,98,2bbl,3.19,3.03,9,70,4800,28,34,8358 164 | 163,0,toyota mark ii,gas,std,four,sedan,fwd,front,95.7,166.3,64.4,52.8,2140,ohc,four,98,2bbl,3.19,3.03,9,70,4800,28,34,9258 165 | 164,1,toyota corolla liftback,gas,std,two,sedan,rwd,front,94.5,168.7,64,52.6,2169,ohc,four,98,2bbl,3.19,3.03,9,70,4800,29,34,8058 166 | 165,1,toyota corona,gas,std,two,hatchback,rwd,front,94.5,168.7,64,52.6,2204,ohc,four,98,2bbl,3.19,3.03,9,70,4800,29,34,8238 167 | 166,1,toyota celica gt liftback,gas,std,two,sedan,rwd,front,94.5,168.7,64,52.6,2265,dohc,four,98,mpfi,3.24,3.08,9.4,112,6600,26,29,9298 168 | 167,1,toyota corolla tercel,gas,std,two,hatchback,rwd,front,94.5,168.7,64,52.6,2300,dohc,four,98,mpfi,3.24,3.08,9.4,112,6600,26,29,9538 169 | 168,2,toyota corona liftback,gas,std,two,hardtop,rwd,front,98.4,176.2,65.6,52,2540,ohc,four,146,mpfi,3.62,3.5,9.3,116,4800,24,30,8449 170 | 169,2,toyota corolla,gas,std,two,hardtop,rwd,front,98.4,176.2,65.6,52,2536,ohc,four,146,mpfi,3.62,3.5,9.3,116,4800,24,30,9639 171 | 170,2,toyota starlet,gas,std,two,hatchback,rwd,front,98.4,176.2,65.6,52,2551,ohc,four,146,mpfi,3.62,3.5,9.3,116,4800,24,30,9989 172 | 171,2,toyota tercel,gas,std,two,hardtop,rwd,front,98.4,176.2,65.6,52,2679,ohc,four,146,mpfi,3.62,3.5,9.3,116,4800,24,30,11199 173 | 172,2,toyota corolla,gas,std,two,hatchback,rwd,front,98.4,176.2,65.6,52,2714,ohc,four,146,mpfi,3.62,3.5,9.3,116,4800,24,30,11549 174 | 173,2,toyota cressida,gas,std,two,convertible,rwd,front,98.4,176.2,65.6,53,2975,ohc,four,146,mpfi,3.62,3.5,9.3,116,4800,24,30,17669 175 | 174,-1,toyota corolla,gas,std,four,sedan,fwd,front,102.4,175.6,66.5,54.9,2326,ohc,four,122,mpfi,3.31,3.54,8.7,92,4200,29,34,8948 176 | 175,-1,toyota celica gt,diesel,turbo,four,sedan,fwd,front,102.4,175.6,66.5,54.9,2480,ohc,four,110,idi,3.27,3.35,22.5,73,4500,30,33,10698 177 | 176,-1,toyota corona,gas,std,four,hatchback,fwd,front,102.4,175.6,66.5,53.9,2414,ohc,four,122,mpfi,3.31,3.54,8.7,92,4200,27,32,9988 178 | 177,-1,toyota corolla,gas,std,four,sedan,fwd,front,102.4,175.6,66.5,54.9,2414,ohc,four,122,mpfi,3.31,3.54,8.7,92,4200,27,32,10898 179 | 178,-1,toyota mark ii,gas,std,four,hatchback,fwd,front,102.4,175.6,66.5,53.9,2458,ohc,four,122,mpfi,3.31,3.54,8.7,92,4200,27,32,11248 180 | 179,3,toyota corolla liftback,gas,std,two,hatchback,rwd,front,102.9,183.5,67.7,52,2976,dohc,six,171,mpfi,3.27,3.35,9.3,161,5200,20,24,16558 181 | 180,3,toyota corona,gas,std,two,hatchback,rwd,front,102.9,183.5,67.7,52,3016,dohc,six,171,mpfi,3.27,3.35,9.3,161,5200,19,24,15998 182 | 181,-1,toyota starlet,gas,std,four,sedan,rwd,front,104.5,187.8,66.5,54.1,3131,dohc,six,171,mpfi,3.27,3.35,9.2,156,5200,20,24,15690 183 | 182,-1,toyouta tercel,gas,std,four,wagon,rwd,front,104.5,187.8,66.5,54.1,3151,dohc,six,161,mpfi,3.27,3.35,9.2,156,5200,19,24,15750 184 | 183,2,vokswagen rabbit,diesel,std,two,sedan,fwd,front,97.3,171.7,65.5,55.7,2261,ohc,four,97,idi,3.01,3.4,23,52,4800,37,46,7775 185 | 184,2,volkswagen 1131 deluxe sedan,gas,std,two,sedan,fwd,front,97.3,171.7,65.5,55.7,2209,ohc,four,109,mpfi,3.19,3.4,9,85,5250,27,34,7975 186 | 185,2,volkswagen model 111,diesel,std,four,sedan,fwd,front,97.3,171.7,65.5,55.7,2264,ohc,four,97,idi,3.01,3.4,23,52,4800,37,46,7995 187 | 186,2,volkswagen type 3,gas,std,four,sedan,fwd,front,97.3,171.7,65.5,55.7,2212,ohc,four,109,mpfi,3.19,3.4,9,85,5250,27,34,8195 188 | 187,2,volkswagen 411 (sw),gas,std,four,sedan,fwd,front,97.3,171.7,65.5,55.7,2275,ohc,four,109,mpfi,3.19,3.4,9,85,5250,27,34,8495 189 | 188,2,volkswagen super beetle,diesel,turbo,four,sedan,fwd,front,97.3,171.7,65.5,55.7,2319,ohc,four,97,idi,3.01,3.4,23,68,4500,37,42,9495 190 | 189,2,volkswagen dasher,gas,std,four,sedan,fwd,front,97.3,171.7,65.5,55.7,2300,ohc,four,109,mpfi,3.19,3.4,10,100,5500,26,32,9995 191 | 190,3,vw dasher,gas,std,two,convertible,fwd,front,94.5,159.3,64.2,55.6,2254,ohc,four,109,mpfi,3.19,3.4,8.5,90,5500,24,29,11595 192 | 191,3,vw rabbit,gas,std,two,hatchback,fwd,front,94.5,165.7,64,51.4,2221,ohc,four,109,mpfi,3.19,3.4,8.5,90,5500,24,29,9980 193 | 192,0,volkswagen rabbit,gas,std,four,sedan,fwd,front,100.4,180.2,66.9,55.1,2661,ohc,five,136,mpfi,3.19,3.4,8.5,110,5500,19,24,13295 194 | 193,0,volkswagen rabbit custom,diesel,turbo,four,sedan,fwd,front,100.4,180.2,66.9,55.1,2579,ohc,four,97,idi,3.01,3.4,23,68,4500,33,38,13845 195 | 194,0,volkswagen dasher,gas,std,four,wagon,fwd,front,100.4,183.1,66.9,55.1,2563,ohc,four,109,mpfi,3.19,3.4,9,88,5500,25,31,12290 196 | 195,-2,volvo 145e (sw),gas,std,four,sedan,rwd,front,104.3,188.8,67.2,56.2,2912,ohc,four,141,mpfi,3.78,3.15,9.5,114,5400,23,28,12940 197 | 196,-1,volvo 144ea,gas,std,four,wagon,rwd,front,104.3,188.8,67.2,57.5,3034,ohc,four,141,mpfi,3.78,3.15,9.5,114,5400,23,28,13415 198 | 197,-2,volvo 244dl,gas,std,four,sedan,rwd,front,104.3,188.8,67.2,56.2,2935,ohc,four,141,mpfi,3.78,3.15,9.5,114,5400,24,28,15985 199 | 198,-1,volvo 245,gas,std,four,wagon,rwd,front,104.3,188.8,67.2,57.5,3042,ohc,four,141,mpfi,3.78,3.15,9.5,114,5400,24,28,16515 200 | 199,-2,volvo 264gl,gas,turbo,four,sedan,rwd,front,104.3,188.8,67.2,56.2,3045,ohc,four,130,mpfi,3.62,3.15,7.5,162,5100,17,22,18420 201 | 200,-1,volvo diesel,gas,turbo,four,wagon,rwd,front,104.3,188.8,67.2,57.5,3157,ohc,four,130,mpfi,3.62,3.15,7.5,162,5100,17,22,18950 202 | 201,-1,volvo 145e (sw),gas,std,four,sedan,rwd,front,109.1,188.8,68.9,55.5,2952,ohc,four,141,mpfi,3.78,3.15,9.5,114,5400,23,28,16845 203 | 202,-1,volvo 144ea,gas,turbo,four,sedan,rwd,front,109.1,188.8,68.8,55.5,3049,ohc,four,141,mpfi,3.78,3.15,8.7,160,5300,19,25,19045 204 | 203,-1,volvo 244dl,gas,std,four,sedan,rwd,front,109.1,188.8,68.9,55.5,3012,ohcv,six,173,mpfi,3.58,2.87,8.8,134,5500,18,23,21485 205 | 204,-1,volvo 246,diesel,turbo,four,sedan,rwd,front,109.1,188.8,68.9,55.5,3217,ohc,six,145,idi,3.01,3.4,23,106,4800,26,27,22470 206 | 205,-1,volvo 264gl,gas,turbo,four,sedan,rwd,front,109.1,188.8,68.9,55.5,3062,ohc,four,141,mpfi,3.78,3.15,9.5,114,5400,19,25,22625 -------------------------------------------------------------------------------- /Chatbot using Python/Chatbot.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# Chatbot using Python" 8 | ] 9 | }, 10 | { 11 | "cell_type": "markdown", 12 | "metadata": {}, 13 | "source": [ 14 | "A Chatbot is an application that interacts with users like a human. Chatbots are typically used to resolve the most common queries that a business receives from its customers daily. In this article, I’ll walk you through how to create a Chatbot using Python." 15 | ] 16 | }, 17 | { 18 | "cell_type": "markdown", 19 | "metadata": {}, 20 | "source": [ 21 | "# What is a Chatbot?" 22 | ] 23 | }, 24 | { 25 | "cell_type": "markdown", 26 | "metadata": {}, 27 | "source": [ 28 | "A Chatbot is an AI application that mimics human conversations. It is widely used by companies to solve the most common problems they receive from customers daily. For example, if you want to know the CRN of your bank account, a chatbot will assist you by asking for your bank details, then it will give you your CRN. But if you want to know something that is not that common, like asking how you can turn your account into a joint account, chances are the authorized employee will assist you." 29 | ] 30 | }, 31 | { 32 | "cell_type": "markdown", 33 | "metadata": {}, 34 | "source": [ 35 | "So while creating a chatbot for any company you should know what that company deals in and what problems their customers get daily. In the section below, I will take you through how to create a chatbot using Python." 36 | ] 37 | }, 38 | { 39 | "cell_type": "markdown", 40 | "metadata": {}, 41 | "source": [ 42 | "# Chatbot with Python" 43 | ] 44 | }, 45 | { 46 | "cell_type": "markdown", 47 | "metadata": {}, 48 | "source": [ 49 | "I hope you now have understood what are chatbots and why so many companies use them to solve the most common problems of their customers. Now let’s see how to create a chatbot with Python. Here, I will be using the NLTK library in Python which is one of the best Python libraries for any task of natural language processing:" 50 | ] 51 | }, 52 | { 53 | "cell_type": "code", 54 | "execution_count": 14, 55 | "metadata": {}, 56 | "outputs": [], 57 | "source": [ 58 | "from nltk.chat.util import Chat, reflections" 59 | ] 60 | }, 61 | { 62 | "cell_type": "markdown", 63 | "metadata": {}, 64 | "source": [ 65 | "#### Now I will be creating a list of queries and their responses for the chatbot" 66 | ] 67 | }, 68 | { 69 | "cell_type": "code", 70 | "execution_count": 24, 71 | "metadata": {}, 72 | "outputs": [], 73 | "source": [ 74 | "#Rasel is a list of patterns and responses.\n", 75 | "rasel = [\n", 76 | " [\n", 77 | " r\"(.*)my name is (.*)\",\n", 78 | " [\"Hello %2, How are you today ?\",]\n", 79 | " ],\n", 80 | " [\n", 81 | " r\"(.*)help(.*) \",\n", 82 | " [\"I can help you \",]\n", 83 | " ],\n", 84 | " [\n", 85 | " r\"(.*) your name ?\",\n", 86 | " [\"My name is CodeWithSheikhRasel, but you can just call me robot and I'm a chatbot .\",]\n", 87 | " ],\n", 88 | " [\n", 89 | " r\"how are you (.*) ?\",\n", 90 | " [\"I'm doing very well\", \"i am great !\"]\n", 91 | " ],\n", 92 | " [\n", 93 | " r\"sorry (.*)\",\n", 94 | " [\"Its alright\",\"Its OK, never mind that\",]\n", 95 | " ],\n", 96 | " [\n", 97 | " r\"i'm (.*) (good|well|okay|ok)\",\n", 98 | " [\"Nice to hear that\",\"Alright, great !\",]\n", 99 | " ],\n", 100 | " [\n", 101 | " r\"(hi|hey|hello|hola|holla)(.*)\",\n", 102 | " [\"Hello\", \"Hey there\",]\n", 103 | " ],\n", 104 | " [\n", 105 | " r\"what (.*) want ?\",\n", 106 | " [\"Make me an offer I can't refuse\",]\n", 107 | " \n", 108 | " ],\n", 109 | " [\n", 110 | " r\"(.*)created(.*)\",\n", 111 | " [\"CodeWithSheikhRasel created me using Python's NLTK library \",\"top secret ;)\",]\n", 112 | " ],\n", 113 | " [\n", 114 | " r\"(.*) (location|city) ?\",\n", 115 | " ['Dhaka, Bangladesh',]\n", 116 | " ],\n", 117 | " [\n", 118 | " r\"(.*)raining in (.*)\",\n", 119 | " [\"No rain in the past 4 days here in %2\",\"In %2 there is a 50% chance of rain\",]\n", 120 | " ],\n", 121 | " [\n", 122 | " r\"how (.*) health (.*)\",\n", 123 | " [\"Health is very important, but I am a computer, so I don't need to worry about my health \",]\n", 124 | " ],\n", 125 | " [\n", 126 | " r\"(.*)(sports|game|sport)(.*)\",\n", 127 | " [\"I'm a very big fan of Football\",]\n", 128 | " ],\n", 129 | " [\n", 130 | " r\"who (.*) (Footballer|Middleman)?\",\n", 131 | " [\"Lionel Messi\"]\n", 132 | " ],\n", 133 | " [\n", 134 | " r\"quit\",\n", 135 | " [\"Bye for now. See you soon :) \",\"It was nice talking to you. See you soon :)\"]\n", 136 | " ],\n", 137 | " [\n", 138 | " r\"(.*)\",\n", 139 | " ['That is nice to hear']\n", 140 | " ],\n", 141 | "]" 142 | ] 143 | }, 144 | { 145 | "cell_type": "markdown", 146 | "metadata": {}, 147 | "source": [ 148 | "Now let’s run this program and interact with the chatbot by using some of the queries related to the queries mentioned in the above list" 149 | ] 150 | }, 151 | { 152 | "cell_type": "code", 153 | "execution_count": 25, 154 | "metadata": {}, 155 | "outputs": [ 156 | { 157 | "name": "stdout", 158 | "output_type": "stream", 159 | "text": [ 160 | "Hi, I'm CodeWithSheikhRasel and I like to chat\n", 161 | "Please type lowercase English language to start a conversation. Type quit to leave \n", 162 | "Hello\n", 163 | "That is nice to hear\n", 164 | "Lionel Messi\n", 165 | "top secret ;)\n", 166 | "CodeWithSheikhRasel created me using Python's NLTK library \n", 167 | "Bye for now. See you soon :) \n" 168 | ] 169 | } 170 | ], 171 | "source": [ 172 | "#default message at the start of chat\n", 173 | "print(\"Hi, I'm CodeWithSheikhRasel and I like to chat\\nPlease type lowercase English language to start a conversation. Type quit to leave \")\n", 174 | "#Create Chat Bot\n", 175 | "chat = Chat(rasel, reflections)\n", 176 | "#Start conversation\n", 177 | "chat.converse()" 178 | ] 179 | }, 180 | { 181 | "cell_type": "markdown", 182 | "metadata": {}, 183 | "source": [ 184 | "So the chatbot performs well. You can create this chatbot more advanced by using some more Python packages of NLP or by adding more queries to it." 185 | ] 186 | }, 187 | { 188 | "cell_type": "markdown", 189 | "metadata": {}, 190 | "source": [ 191 | "# Summary" 192 | ] 193 | }, 194 | { 195 | "cell_type": "markdown", 196 | "metadata": {}, 197 | "source": [ 198 | "Chatbots are very useful applications for every company especially if a company is providing any type of service. It helps an organization by solving the most common queries of the customers. I hope you liked this article on how to create a chatbot using Python. Feel free to ask your valuable questions in the comments section below." 199 | ] 200 | }, 201 | { 202 | "cell_type": "markdown", 203 | "metadata": {}, 204 | "source": [ 205 | "# Sheikh Rasel Ahmed" 206 | ] 207 | }, 208 | { 209 | "cell_type": "markdown", 210 | "metadata": {}, 211 | "source": [ 212 | "#### Data Science || Machine Learning || Deep Learning || Artificial Intelligence Enthusiast" 213 | ] 214 | }, 215 | { 216 | "cell_type": "markdown", 217 | "metadata": {}, 218 | "source": [ 219 | "###### LinkedIn - https://www.linkedin.com/in/shekhnirob1\n", 220 | "\n", 221 | "###### GitHub - https://github.com/Rasel1435\n", 222 | "\n", 223 | "###### Behance - https://www.behance.net/Shekhrasel2513" 224 | ] 225 | } 226 | ], 227 | "metadata": { 228 | "kernelspec": { 229 | "display_name": "Python 3.10.5 64-bit", 230 | "language": "python", 231 | "name": "python3" 232 | }, 233 | "language_info": { 234 | "codemirror_mode": { 235 | "name": "ipython", 236 | "version": 3 237 | }, 238 | "file_extension": ".py", 239 | "mimetype": "text/x-python", 240 | "name": "python", 241 | "nbconvert_exporter": "python", 242 | "pygments_lexer": "ipython3", 243 | "version": "3.10.5" 244 | }, 245 | "orig_nbformat": 4, 246 | "vscode": { 247 | "interpreter": { 248 | "hash": "afb734500600fd355917ca529030176ea0ca205570884b88f2f6f7d791fd3fbe" 249 | } 250 | } 251 | }, 252 | "nbformat": 4, 253 | "nbformat_minor": 2 254 | } 255 | -------------------------------------------------------------------------------- /Click-Through Rate Prediction with Machine Learning/data/advertising.csv: -------------------------------------------------------------------------------- 1 | "","TV","Radio","Newspaper","Sales" 2 | "1",230.1,37.8,69.2,22.1 3 | "2",44.5,39.3,45.1,10.4 4 | "3",17.2,45.9,69.3,9.3 5 | "4",151.5,41.3,58.5,18.5 6 | "5",180.8,10.8,58.4,12.9 7 | "6",8.7,48.9,75,7.2 8 | "7",57.5,32.8,23.5,11.8 9 | "8",120.2,19.6,11.6,13.2 10 | "9",8.6,2.1,1,4.8 11 | "10",199.8,2.6,21.2,10.6 12 | "11",66.1,5.8,24.2,8.6 13 | "12",214.7,24,4,17.4 14 | "13",23.8,35.1,65.9,9.2 15 | "14",97.5,7.6,7.2,9.7 16 | "15",204.1,32.9,46,19 17 | "16",195.4,47.7,52.9,22.4 18 | "17",67.8,36.6,114,12.5 19 | "18",281.4,39.6,55.8,24.4 20 | "19",69.2,20.5,18.3,11.3 21 | "20",147.3,23.9,19.1,14.6 22 | "21",218.4,27.7,53.4,18 23 | "22",237.4,5.1,23.5,12.5 24 | "23",13.2,15.9,49.6,5.6 25 | "24",228.3,16.9,26.2,15.5 26 | "25",62.3,12.6,18.3,9.7 27 | "26",262.9,3.5,19.5,12 28 | "27",142.9,29.3,12.6,15 29 | "28",240.1,16.7,22.9,15.9 30 | "29",248.8,27.1,22.9,18.9 31 | "30",70.6,16,40.8,10.5 32 | "31",292.9,28.3,43.2,21.4 33 | "32",112.9,17.4,38.6,11.9 34 | "33",97.2,1.5,30,9.6 35 | "34",265.6,20,0.3,17.4 36 | "35",95.7,1.4,7.4,9.5 37 | "36",290.7,4.1,8.5,12.8 38 | "37",266.9,43.8,5,25.4 39 | "38",74.7,49.4,45.7,14.7 40 | "39",43.1,26.7,35.1,10.1 41 | "40",228,37.7,32,21.5 42 | "41",202.5,22.3,31.6,16.6 43 | "42",177,33.4,38.7,17.1 44 | "43",293.6,27.7,1.8,20.7 45 | "44",206.9,8.4,26.4,12.9 46 | "45",25.1,25.7,43.3,8.5 47 | "46",175.1,22.5,31.5,14.9 48 | "47",89.7,9.9,35.7,10.6 49 | "48",239.9,41.5,18.5,23.2 50 | "49",227.2,15.8,49.9,14.8 51 | "50",66.9,11.7,36.8,9.7 52 | "51",199.8,3.1,34.6,11.4 53 | "52",100.4,9.6,3.6,10.7 54 | "53",216.4,41.7,39.6,22.6 55 | "54",182.6,46.2,58.7,21.2 56 | "55",262.7,28.8,15.9,20.2 57 | "56",198.9,49.4,60,23.7 58 | "57",7.3,28.1,41.4,5.5 59 | "58",136.2,19.2,16.6,13.2 60 | "59",210.8,49.6,37.7,23.8 61 | "60",210.7,29.5,9.3,18.4 62 | "61",53.5,2,21.4,8.1 63 | "62",261.3,42.7,54.7,24.2 64 | "63",239.3,15.5,27.3,15.7 65 | "64",102.7,29.6,8.4,14 66 | "65",131.1,42.8,28.9,18 67 | "66",69,9.3,0.9,9.3 68 | "67",31.5,24.6,2.2,9.5 69 | "68",139.3,14.5,10.2,13.4 70 | "69",237.4,27.5,11,18.9 71 | "70",216.8,43.9,27.2,22.3 72 | "71",199.1,30.6,38.7,18.3 73 | "72",109.8,14.3,31.7,12.4 74 | "73",26.8,33,19.3,8.8 75 | "74",129.4,5.7,31.3,11 76 | "75",213.4,24.6,13.1,17 77 | "76",16.9,43.7,89.4,8.7 78 | "77",27.5,1.6,20.7,6.9 79 | "78",120.5,28.5,14.2,14.2 80 | "79",5.4,29.9,9.4,5.3 81 | "80",116,7.7,23.1,11 82 | "81",76.4,26.7,22.3,11.8 83 | "82",239.8,4.1,36.9,12.3 84 | "83",75.3,20.3,32.5,11.3 85 | "84",68.4,44.5,35.6,13.6 86 | "85",213.5,43,33.8,21.7 87 | "86",193.2,18.4,65.7,15.2 88 | "87",76.3,27.5,16,12 89 | "88",110.7,40.6,63.2,16 90 | "89",88.3,25.5,73.4,12.9 91 | "90",109.8,47.8,51.4,16.7 92 | "91",134.3,4.9,9.3,11.2 93 | "92",28.6,1.5,33,7.3 94 | "93",217.7,33.5,59,19.4 95 | "94",250.9,36.5,72.3,22.2 96 | "95",107.4,14,10.9,11.5 97 | "96",163.3,31.6,52.9,16.9 98 | "97",197.6,3.5,5.9,11.7 99 | "98",184.9,21,22,15.5 100 | "99",289.7,42.3,51.2,25.4 101 | "100",135.2,41.7,45.9,17.2 102 | "101",222.4,4.3,49.8,11.7 103 | "102",296.4,36.3,100.9,23.8 104 | "103",280.2,10.1,21.4,14.8 105 | "104",187.9,17.2,17.9,14.7 106 | "105",238.2,34.3,5.3,20.7 107 | "106",137.9,46.4,59,19.2 108 | "107",25,11,29.7,7.2 109 | "108",90.4,0.3,23.2,8.7 110 | "109",13.1,0.4,25.6,5.3 111 | "110",255.4,26.9,5.5,19.8 112 | "111",225.8,8.2,56.5,13.4 113 | "112",241.7,38,23.2,21.8 114 | "113",175.7,15.4,2.4,14.1 115 | "114",209.6,20.6,10.7,15.9 116 | "115",78.2,46.8,34.5,14.6 117 | "116",75.1,35,52.7,12.6 118 | "117",139.2,14.3,25.6,12.2 119 | "118",76.4,0.8,14.8,9.4 120 | "119",125.7,36.9,79.2,15.9 121 | "120",19.4,16,22.3,6.6 122 | "121",141.3,26.8,46.2,15.5 123 | "122",18.8,21.7,50.4,7 124 | "123",224,2.4,15.6,11.6 125 | "124",123.1,34.6,12.4,15.2 126 | "125",229.5,32.3,74.2,19.7 127 | "126",87.2,11.8,25.9,10.6 128 | "127",7.8,38.9,50.6,6.6 129 | "128",80.2,0,9.2,8.8 130 | "129",220.3,49,3.2,24.7 131 | "130",59.6,12,43.1,9.7 132 | "131",0.7,39.6,8.7,1.6 133 | "132",265.2,2.9,43,12.7 134 | "133",8.4,27.2,2.1,5.7 135 | "134",219.8,33.5,45.1,19.6 136 | "135",36.9,38.6,65.6,10.8 137 | "136",48.3,47,8.5,11.6 138 | "137",25.6,39,9.3,9.5 139 | "138",273.7,28.9,59.7,20.8 140 | "139",43,25.9,20.5,9.6 141 | "140",184.9,43.9,1.7,20.7 142 | "141",73.4,17,12.9,10.9 143 | "142",193.7,35.4,75.6,19.2 144 | "143",220.5,33.2,37.9,20.1 145 | "144",104.6,5.7,34.4,10.4 146 | "145",96.2,14.8,38.9,11.4 147 | "146",140.3,1.9,9,10.3 148 | "147",240.1,7.3,8.7,13.2 149 | "148",243.2,49,44.3,25.4 150 | "149",38,40.3,11.9,10.9 151 | "150",44.7,25.8,20.6,10.1 152 | "151",280.7,13.9,37,16.1 153 | "152",121,8.4,48.7,11.6 154 | "153",197.6,23.3,14.2,16.6 155 | "154",171.3,39.7,37.7,19 156 | "155",187.8,21.1,9.5,15.6 157 | "156",4.1,11.6,5.7,3.2 158 | "157",93.9,43.5,50.5,15.3 159 | "158",149.8,1.3,24.3,10.1 160 | "159",11.7,36.9,45.2,7.3 161 | "160",131.7,18.4,34.6,12.9 162 | "161",172.5,18.1,30.7,14.4 163 | "162",85.7,35.8,49.3,13.3 164 | "163",188.4,18.1,25.6,14.9 165 | "164",163.5,36.8,7.4,18 166 | "165",117.2,14.7,5.4,11.9 167 | "166",234.5,3.4,84.8,11.9 168 | "167",17.9,37.6,21.6,8 169 | "168",206.8,5.2,19.4,12.2 170 | "169",215.4,23.6,57.6,17.1 171 | "170",284.3,10.6,6.4,15 172 | "171",50,11.6,18.4,8.4 173 | "172",164.5,20.9,47.4,14.5 174 | "173",19.6,20.1,17,7.6 175 | "174",168.4,7.1,12.8,11.7 176 | "175",222.4,3.4,13.1,11.5 177 | "176",276.9,48.9,41.8,27 178 | "177",248.4,30.2,20.3,20.2 179 | "178",170.2,7.8,35.2,11.7 180 | "179",276.7,2.3,23.7,11.8 181 | "180",165.6,10,17.6,12.6 182 | "181",156.6,2.6,8.3,10.5 183 | "182",218.5,5.4,27.4,12.2 184 | "183",56.2,5.7,29.7,8.7 185 | "184",287.6,43,71.8,26.2 186 | "185",253.8,21.3,30,17.6 187 | "186",205,45.1,19.6,22.6 188 | "187",139.5,2.1,26.6,10.3 189 | "188",191.1,28.7,18.2,17.3 190 | "189",286,13.9,3.7,15.9 191 | "190",18.7,12.1,23.4,6.7 192 | "191",39.5,41.1,5.8,10.8 193 | "192",75.5,10.8,6,9.9 194 | "193",17.2,4.1,31.6,5.9 195 | "194",166.8,42,3.6,19.6 196 | "195",149.7,35.6,6,17.3 197 | "196",38.2,3.7,13.8,7.6 198 | "197",94.2,4.9,8.1,9.7 199 | "198",177,9.3,6.4,12.8 200 | "199",283.6,42,66.2,25.5 201 | "200",232.1,8.6,8.7,13.4 -------------------------------------------------------------------------------- /Click-Through Rate Prediction with Machine Learning/img/Newspaper.png: -------------------------------------------------------------------------------- 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https://raw.githubusercontent.com/Rasel1435/Machine-Learning-Projects-with-Python-for-Beginners/60231f07b76f0fe200226cc383857c22d2fed571/Click-Through Rate Prediction with Machine Learning/img/TV.png -------------------------------------------------------------------------------- /Count Objects in Image using Python/data/Capture.JPG: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Rasel1435/Machine-Learning-Projects-with-Python-for-Beginners/60231f07b76f0fe200226cc383857c22d2fed571/Count Objects in Image using Python/data/Capture.JPG -------------------------------------------------------------------------------- /Count Objects in Image using Python/data/cars.webp: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Rasel1435/Machine-Learning-Projects-with-Python-for-Beginners/60231f07b76f0fe200226cc383857c22d2fed571/Count Objects in Image using Python/data/cars.webp -------------------------------------------------------------------------------- /Covid-19 Vaccines Analysis with Python/final_output.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Rasel1435/Machine-Learning-Projects-with-Python-for-Beginners/60231f07b76f0fe200226cc383857c22d2fed571/Covid-19 Vaccines Analysis with Python/final_output.png -------------------------------------------------------------------------------- /Data Science Project on President Heights/data/heights.csv: -------------------------------------------------------------------------------- 1 | order,name,height(cm) 2 | 1,George Washington,189 3 | 2,John Adams,170 4 | 3,Thomas Jefferson,189 5 | 4,James Madison,163 6 | 5,James Monroe,183 7 | 6,John Quincy Adams,171 8 | 7,Andrew Jackson,185 9 | 8,Martin Van Buren,168 10 | 9,William Henry Harrison,173 11 | 10,John Tyler,183 12 | 11,James K. Polk,173 13 | 12,Zachary Taylor,173 14 | 13,Millard Fillmore,175 15 | 14,Franklin Pierce,178 16 | 15,James Buchanan,183 17 | 16,Abraham Lincoln,193 18 | 17,Andrew Johnson,178 19 | 18,Ulysses S. Grant,173 20 | 19,Rutherford B. Hayes,174 21 | 20,James A. Garfield,183 22 | 21,Chester A. Arthur,183 23 | 23,Benjamin Harrison,168 24 | 25,William McKinley,170 25 | 26,Theodore Roosevelt,178 26 | 27,William Howard Taft,182 27 | 28,Woodrow Wilson,180 28 | 29,Warren G. Harding,183 29 | 30,Calvin Coolidge,178 30 | 31,Herbert Hoover,182 31 | 32,Franklin D. Roosevelt,188 32 | 33,Harry S. Truman,175 33 | 34,Dwight D. Eisenhower,179 34 | 35,John F. Kennedy,183 35 | 36,Lyndon B. Johnson,193 36 | 37,Richard Nixon,182 37 | 38,Gerald Ford,183 38 | 39,Jimmy Carter,177 39 | 40,Ronald Reagan,185 40 | 41,George H. W. Bush,188 41 | 42,Bill Clinton,188 42 | 43,George W. Bush,182 43 | 44,Barack Obama,185 -------------------------------------------------------------------------------- /Dogecoin Price Prediction with Machine Learning/data/DOGE-USD.csv: -------------------------------------------------------------------------------- 1 | Date,Open,High,Low,Close,Adj Close,Volume 2 | 2021-12-11,0.164499,0.170080,0.161817,0.169019,0.169019,692929162 3 | 2021-12-12,0.168904,0.172899,0.165899,0.169891,0.169891,593132337 4 | 2021-12-13,0.169880,0.170615,0.151996,0.157565,0.157565,968464045 5 | 2021-12-14,0.157465,0.219632,0.155447,0.180157,0.180157,6411471551 6 | 2021-12-15,0.191144,0.191144,0.171266,0.181082,0.181082,2466135011 7 | 2021-12-16,0.181171,0.184933,0.172655,0.173454,0.173454,1057988990 8 | 2021-12-17,0.173381,0.176559,0.162263,0.169010,0.169010,1026719015 9 | 2021-12-18,0.169100,0.174874,0.166244,0.172400,0.172400,658993272 10 | 2021-12-19,0.172533,0.174378,0.169382,0.169576,0.169576,541018118 11 | 2021-12-20,0.169523,0.170833,0.161497,0.167322,0.167322,912233179 12 | 2021-12-21,0.167098,0.172340,0.166108,0.171175,0.171175,621397179 13 | 2021-12-22,0.171129,0.179585,0.170055,0.173214,0.173214,932537493 14 | 2021-12-23,0.173209,0.184522,0.171740,0.184490,0.184490,1184481683 15 | 2021-12-24,0.184979,0.195290,0.179761,0.186622,0.186622,1853415104 16 | 2021-12-25,0.186712,0.194876,0.185571,0.190657,0.190657,1010443046 17 | 2021-12-26,0.190567,0.192546,0.185646,0.190020,0.190020,650674078 18 | 2021-12-27,0.189986,0.192923,0.187239,0.187705,0.187705,666773423 19 | 2021-12-28,0.187847,0.187885,0.172738,0.174117,0.174117,954075337 20 | 2021-12-29,0.174095,0.177339,0.166760,0.167765,0.167765,823628336 21 | 2021-12-30,0.168137,0.175771,0.165381,0.171313,0.171313,599236772 22 | 2021-12-31,0.171317,0.175294,0.167307,0.170496,0.170496,644416694 23 | 2022-01-01,0.170510,0.173423,0.170353,0.173035,0.173035,371336089 24 | 2022-01-02,0.173027,0.175989,0.171201,0.174403,0.174403,391041933 25 | 2022-01-03,0.174406,0.174406,0.168271,0.170088,0.170088,505900382 26 | 2022-01-04,0.170151,0.172339,0.168128,0.168803,0.168803,541922892 27 | 2022-01-05,0.168835,0.170747,0.151898,0.159420,0.159420,994086848 28 | 2022-01-06,0.159436,0.161706,0.155142,0.160213,0.160213,715345645 29 | 2022-01-07,0.160205,0.160295,0.151299,0.155023,0.155023,969384523 30 | 2022-01-08,0.155031,0.157725,0.147669,0.151954,0.151954,584437036 31 | 2022-01-09,0.151959,0.153742,0.149607,0.151065,0.151065,408786249 32 | 2022-01-10,0.151073,0.152135,0.139840,0.143359,0.143359,763684873 33 | 2022-01-11,0.143351,0.164621,0.142647,0.153399,0.153399,1628447666 34 | 2022-01-12,0.153458,0.161652,0.152095,0.161652,0.161652,896008600 35 | 2022-01-13,0.161562,0.174792,0.161562,0.172043,0.172043,2301820074 36 | 2022-01-14,0.172053,0.203232,0.171283,0.183549,0.183549,5784004926 37 | 2022-01-15,0.183644,0.193600,0.182676,0.185103,0.185103,1878282290 38 | 2022-01-16,0.185093,0.185093,0.175327,0.177176,0.177176,1102750171 39 | 2022-01-17,0.177187,0.177872,0.166891,0.171145,0.171145,1094379303 40 | 2022-01-18,0.171086,0.173790,0.162565,0.165485,0.165485,979693368 41 | 2022-01-19,0.165485,0.168647,0.160122,0.162356,0.162356,935006164 42 | 2022-01-20,0.162346,0.168846,0.155176,0.155204,0.155204,469269658 43 | 2022-01-21,0.155263,0.157156,0.141496,0.142640,0.142640,1163061854 44 | 2022-01-22,0.142651,0.145027,0.122816,0.132892,0.132892,1693524581 45 | 2022-01-23,0.132960,0.143072,0.132819,0.141863,0.141863,1006234946 46 | 2022-01-24,0.141881,0.141951,0.127220,0.137798,0.137798,1446873574 47 | 2022-01-25,0.137784,0.147236,0.133235,0.143049,0.143049,1347567750 48 | 2022-01-26,0.143014,0.152603,0.142152,0.143789,0.143789,1253108115 49 | 2022-01-27,0.143756,0.145010,0.136947,0.141247,0.141247,608707586 50 | 2022-01-28,0.141244,0.142413,0.138380,0.141656,0.141656,506667466 51 | 2022-01-29,0.141649,0.143984,0.141480,0.143056,0.143056,397610776 52 | 2022-01-30,0.143057,0.143515,0.138754,0.139471,0.139471,363976741 53 | 2022-01-31,0.139469,0.142264,0.136853,0.141805,0.141805,412307174 54 | 2022-02-01,0.141805,0.144129,0.141125,0.142631,0.142631,409432267 55 | 2022-02-02,0.142626,0.145253,0.136918,0.137235,0.137235,483194691 56 | 2022-02-03,0.137213,0.138747,0.135565,0.137541,0.137541,383506507 57 | 2022-02-04,0.137523,0.147592,0.137250,0.147503,0.147503,580740990 58 | 2022-02-05,0.147533,0.150140,0.145410,0.147141,0.147141,587009429 59 | 2022-02-06,0.147140,0.154775,0.145258,0.153770,0.153770,757004142 60 | 2022-02-07,0.153803,0.171586,0.151741,0.165578,0.165578,1791358822 61 | 2022-02-08,0.165606,0.169145,0.153709,0.158405,0.158405,1190116000 62 | 2022-02-09,0.158357,0.160704,0.155035,0.159123,0.159123,778670798 63 | 2022-02-10,0.159145,0.160437,0.151497,0.151889,0.151889,1053630584 64 | 2022-02-11,0.151895,0.154069,0.143349,0.144847,0.144847,776730555 65 | 2022-02-12,0.144856,0.147013,0.141678,0.144405,0.144405,602699408 66 | 2022-02-13,0.144379,0.157354,0.144031,0.148948,0.148948,1581065491 67 | 2022-02-14,0.148869,0.150132,0.143649,0.146003,0.146003,898042727 68 | 2022-02-15,0.145996,0.152199,0.145203,0.151761,0.151761,674961496 69 | 2022-02-16,0.151779,0.151824,0.146771,0.149095,0.149095,505588732 70 | 2022-02-17,0.149050,0.149889,0.138071,0.138552,0.138552,721382126 71 | 2022-02-18,0.138594,0.142097,0.136960,0.138768,0.138768,563817289 72 | 2022-02-19,0.138764,0.145906,0.138751,0.141206,0.141206,655782652 73 | 2022-02-20,0.141197,0.141197,0.135852,0.136868,0.136868,490138547 74 | 2022-02-21,0.136838,0.141345,0.128246,0.128490,0.128490,913773124 75 | 2022-02-22,0.128488,0.131696,0.125365,0.131553,0.131553,711967119 76 | 2022-02-23,0.131561,0.135337,0.127846,0.127846,0.127846,587472906 77 | 2022-02-24,0.127821,0.127851,0.109555,0.123813,0.123813,2046477370 78 | 2022-02-25,0.123843,0.128181,0.120983,0.127576,0.127576,805161073 79 | 2022-02-26,0.127568,0.130071,0.126199,0.127647,0.127647,535575654 80 | 2022-02-27,0.127639,0.128235,0.122053,0.123111,0.123111,592809151 81 | 2022-02-28,0.123118,0.134479,0.121954,0.133156,0.133156,765755924 82 | 2022-03-01,0.133174,0.137730,0.130462,0.133938,0.133938,876604512 83 | 2022-03-02,0.133964,0.136214,0.131232,0.132990,0.132990,709536320 84 | 2022-03-03,0.132998,0.133649,0.127810,0.129610,0.129610,518193386 85 | 2022-03-04,0.129631,0.129856,0.121544,0.122591,0.122591,650665594 86 | 2022-03-05,0.122614,0.125375,0.121572,0.124996,0.124996,334091552 87 | 2022-03-06,0.125025,0.125611,0.120384,0.120766,0.120766,400458464 88 | 2022-03-07,0.120769,0.122563,0.115015,0.117105,0.117105,513014829 89 | 2022-03-08,0.117106,0.119724,0.115838,0.117029,0.117029,491414294 90 | 2022-03-09,0.117047,0.123734,0.116766,0.121588,0.121588,519157507 91 | 2022-03-10,0.121572,0.121612,0.115532,0.116885,0.116885,513641345 92 | 2022-03-11,0.116893,0.118293,0.114570,0.115602,0.115602,388727358 93 | 2022-03-12,0.115608,0.117239,0.115132,0.115136,0.115136,235277001 94 | 2022-03-13,0.115080,0.116231,0.110686,0.111608,0.111608,373430106 95 | 2022-03-14,0.111607,0.118967,0.110817,0.114048,0.114048,822092169 96 | 2022-03-15,0.114043,0.114213,0.110937,0.112784,0.112784,400614617 97 | 2022-03-16,0.112779,0.116908,0.112364,0.116908,0.116908,537170937 98 | 2022-03-17,0.116907,0.118710,0.116281,0.116502,0.116502,412838814 99 | 2022-03-18,0.116490,0.119375,0.114568,0.119306,0.119306,410862503 100 | 2022-03-19,0.119278,0.124573,0.118530,0.123569,0.123569,628081786 101 | 2022-03-20,0.123579,0.123919,0.117991,0.119154,0.119154,428111799 102 | 2022-03-21,0.119146,0.121332,0.118306,0.119339,0.119339,439486516 103 | 2022-03-22,0.119333,0.125350,0.119014,0.122481,0.122481,610507111 104 | 2022-03-23,0.122487,0.130713,0.121361,0.129727,0.129727,998922753 105 | 2022-03-24,0.129722,0.140605,0.128455,0.136550,0.136550,2017926806 106 | 2022-03-25,0.136603,0.137275,0.128782,0.131013,0.131013,882486375 107 | 2022-03-26,0.131010,0.136495,0.129878,0.135868,0.135868,610401998 108 | 2022-03-27,0.135900,0.144858,0.135703,0.144732,0.144732,1445019558 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2022-04-10,0.144304,0.156972,0.143863,0.149535,0.149535,1931829669 123 | 2022-04-11,0.149374,0.151292,0.133818,0.134654,0.134654,1515679359 124 | 2022-04-12,0.134652,0.144122,0.134243,0.138076,0.138076,1214105682 125 | 2022-04-13,0.138070,0.142608,0.137067,0.140080,0.140080,890728707 126 | 2022-04-14,0.140053,0.146633,0.138101,0.143920,0.143920,1759067761 127 | 2022-04-15,0.143917,0.149287,0.142767,0.146026,0.146026,1140382087 128 | 2022-04-16,0.146017,0.147811,0.142093,0.143712,0.143712,584019179 129 | 2022-04-17,0.143693,0.144709,0.138995,0.139459,0.139459,636442285 130 | 2022-04-18,0.139471,0.140461,0.134384,0.140286,0.140286,909718484 131 | 2022-04-19,0.140297,0.144141,0.139500,0.142665,0.142665,679511647 132 | 2022-04-20,0.142677,0.146241,0.139595,0.140878,0.140878,1068542289 133 | 2022-04-21,0.140868,0.142448,0.135255,0.136365,0.136365,740549793 134 | 2022-04-22,0.136357,0.137908,0.134985,0.136395,0.136395,505251263 135 | 2022-04-23,0.136387,0.136495,0.133810,0.134241,0.134241,349740069 136 | 2022-04-24,0.134202,0.134690,0.131502,0.131947,0.131947,436620221 137 | 2022-04-25,0.131936,0.167735,0.123869,0.157959,0.157959,5177823685 138 | 2022-04-26,0.157777,0.165278,0.137424,0.137767,0.137767,4529010503 139 | 2022-04-27,0.137847,0.145405,0.136272,0.139700,0.139700,1765370972 140 | 2022-04-28,0.139691,0.142618,0.136058,0.137363,0.137363,1105137506 141 | 2022-04-29,0.137376,0.144643,0.134099,0.135027,0.135027,1555397213 142 | 2022-04-30,0.135033,0.136528,0.126239,0.127557,0.127557,916612071 143 | 2022-05-01,0.127589,0.134821,0.127533,0.132773,0.132773,991710768 144 | 2022-05-02,0.132794,0.133783,0.128300,0.130938,0.130938,769062732 145 | 2022-05-03,0.130935,0.131833,0.127399,0.129520,0.129520,555706527 146 | 2022-05-04,0.129514,0.135809,0.129313,0.135809,0.135809,838050627 147 | 2022-05-05,0.135813,0.137313,0.126697,0.128430,0.128430,1258066467 148 | 2022-05-06,0.128403,0.129305,0.124566,0.127901,0.127901,839005988 149 | 2022-05-07,0.127890,0.131920,0.125365,0.127532,0.127532,746383231 150 | 2022-05-08,0.127527,0.128017,0.123974,0.124207,0.124207,728953161 151 | 2022-05-09,0.124200,0.125136,0.104824,0.104824,0.104824,1353326367 152 | 2022-05-10,0.104708,0.117775,0.100881,0.108477,0.108477,1723066438 153 | 2022-05-11,0.108449,0.110565,0.080036,0.084581,0.084581,2556008051 154 | 2022-05-12,0.084636,0.089028,0.070037,0.082671,0.082671,2631064083 155 | 2022-05-13,0.082639,0.094254,0.082371,0.088331,0.088331,1444350618 156 | 2022-05-14,0.088341,0.091598,0.084483,0.089796,0.089796,902479951 157 | 2022-05-15,0.089785,0.092843,0.086876,0.092843,0.092843,651900826 158 | 2022-05-16,0.092867,0.093061,0.086428,0.087841,0.087841,606521740 159 | 2022-05-17,0.087836,0.091112,0.087484,0.090213,0.090213,560800173 160 | 2022-05-18,0.090221,0.090797,0.083649,0.083649,0.083649,558257804 161 | 2022-05-19,0.083635,0.087124,0.082087,0.086723,0.086723,613801947 162 | 2022-05-20,0.086719,0.087750,0.082859,0.083977,0.083977,518556378 163 | 2022-05-21,0.083972,0.085110,0.082932,0.084463,0.084463,335246011 164 | 2022-05-22,0.084448,0.086480,0.084058,0.086105,0.086105,375026021 165 | 2022-05-23,0.086084,0.088110,0.082858,0.083253,0.083253,507385171 166 | 2022-05-24,0.083267,0.084317,0.080811,0.083637,0.083637,462415724 167 | 2022-05-25,0.083628,0.084338,0.082319,0.082985,0.082985,419834752 168 | 2022-05-26,0.082994,0.083600,0.076065,0.078252,0.078252,711890873 169 | 2022-05-27,0.078211,0.085384,0.076582,0.081335,0.081335,1310057650 170 | 2022-05-28,0.082248,0.083048,0.081151,0.081839,0.081839,535539620 171 | 2022-05-29,0.081838,0.083034,0.080299,0.082686,0.082686,394962471 172 | 2022-05-30,0.082684,0.088275,0.082269,0.087871,0.087871,690696756 173 | 2022-05-31,0.087865,0.088230,0.083612,0.085865,0.085865,672444588 174 | 2022-06-01,0.085855,0.088647,0.079807,0.081056,0.081056,830748610 175 | 2022-06-02,0.081052,0.083007,0.080417,0.082772,0.082772,434510607 176 | 2022-06-03,0.082777,0.082857,0.079756,0.080442,0.080442,403339248 177 | 2022-06-04,0.080454,0.082337,0.079727,0.081777,0.081777,350329772 178 | 2022-06-05,0.081776,0.082138,0.080836,0.081229,0.081229,282624184 179 | 2022-06-06,0.081225,0.084559,0.081126,0.082530,0.082530,503707292 180 | 2022-06-07,0.082530,0.082530,0.078332,0.080513,0.080513,509642725 181 | 2022-06-08,0.080492,0.081420,0.079156,0.079439,0.079439,328046389 182 | 2022-06-09,0.079436,0.080898,0.079082,0.079336,0.079336,274673852 183 | 2022-06-10,0.079335,0.079521,0.074804,0.075454,0.075454,431520761 184 | 2022-06-11,0.075472,0.076399,0.068596,0.069895,0.069895,497184012 185 | 2022-06-12,0.069919,0.070345,0.063861,0.064336,0.064336,708156957 186 | 2022-06-13,0.064275,0.065702,0.053002,0.053921,0.053921,1465281537 187 | 2022-06-14,0.053941,0.058154,0.050267,0.055592,0.055592,908921329 188 | 2022-06-15,0.055577,0.063472,0.050670,0.063000,0.063000,1122284662 189 | 2022-06-16,0.062977,0.062977,0.054508,0.055029,0.055029,724479688 190 | 2022-06-17,0.055047,0.057911,0.054898,0.056995,0.056995,383830484 191 | 2022-06-18,0.056979,0.057755,0.049720,0.053012,0.053012,652345943 192 | 2022-06-19,0.053007,0.062472,0.051308,0.060396,0.060396,1166163984 193 | 2022-06-20,0.060399,0.061157,0.057707,0.060177,0.060177,499018791 194 | 2022-06-21,0.060175,0.069324,0.059315,0.065571,0.065571,1051899538 195 | 2022-06-22,0.065606,0.065751,0.061389,0.061711,0.061711,633619298 196 | 2022-06-23,0.061703,0.064615,0.061703,0.064032,0.064032,434169751 197 | 2022-06-24,0.064019,0.068162,0.064004,0.067124,0.067124,514729444 198 | 2022-06-25,0.067110,0.069112,0.065384,0.068503,0.068503,432671864 199 | 2022-06-26,0.068488,0.076966,0.067504,0.073177,0.073177,883689355 200 | 2022-06-27,0.073154,0.078389,0.071018,0.072239,0.072239,962250061 201 | 2022-06-28,0.072221,0.072951,0.065723,0.065957,0.065957,534793983 202 | 2022-06-29,0.065996,0.071335,0.064635,0.069468,0.069468,592828738 203 | 2022-06-30,0.069481,0.069513,0.062746,0.066086,0.066086,566648029 204 | 2022-07-01,0.066208,0.067862,0.064224,0.066175,0.066175,472441458 205 | 2022-07-02,0.066205,0.067692,0.065933,0.066698,0.066698,352589827 206 | 2022-07-03,0.066698,0.067356,0.065111,0.067179,0.067179,280878240 207 | 2022-07-04,0.067174,0.069605,0.065927,0.069472,0.069472,379581122 208 | 2022-07-05,0.069431,0.069841,0.065697,0.067155,0.067155,403220687 209 | 2022-07-06,0.067172,0.069058,0.066448,0.068583,0.068583,398926503 210 | 2022-07-07,0.068579,0.071124,0.068053,0.070621,0.070621,402591742 211 | 2022-07-08,0.070618,0.073009,0.068541,0.069257,0.069257,444369733 212 | 2022-07-09,0.069224,0.070009,0.068995,0.069512,0.069512,254272147 213 | 2022-07-10,0.069509,0.069509,0.066533,0.067263,0.067263,299810124 214 | 2022-07-11,0.067268,0.067268,0.061535,0.061847,0.061847,326125900 215 | 2022-07-12,0.061851,0.062861,0.060087,0.060087,0.060087,298462083 216 | 2022-07-13,0.060085,0.062011,0.058398,0.061953,0.061953,446505400 217 | 2022-07-14,0.061933,0.062632,0.059741,0.062394,0.062394,348090510 218 | 2022-07-15,0.062400,0.064259,0.061747,0.063042,0.063042,374357844 219 | 2022-07-16,0.063037,0.064725,0.061870,0.064280,0.064280,279644299 220 | 2022-07-17,0.064288,0.065394,0.063101,0.063107,0.063107,249697026 221 | 2022-07-18,0.063114,0.068674,0.063114,0.067715,0.067715,564795959 222 | 2022-07-19,0.067690,0.069900,0.065752,0.069013,0.069013,552211168 223 | 2022-07-20,0.069015,0.076574,0.067928,0.070120,0.070120,1019375521 224 | 2022-07-21,0.070124,0.070732,0.067237,0.069960,0.069960,492434239 225 | 2022-07-22,0.069970,0.071270,0.067003,0.067563,0.067563,479456105 226 | 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2022-08-05,0.067333,0.070117,0.067256,0.069765,0.069765,336216727 240 | 2022-08-06,0.069766,0.070999,0.068586,0.068674,0.068674,277979112 241 | 2022-08-07,0.068669,0.069899,0.067747,0.068940,0.068940,204862521 242 | 2022-08-08,0.068946,0.072058,0.068923,0.070055,0.070055,399183407 243 | 2022-08-09,0.070061,0.073886,0.068462,0.069125,0.069125,611327142 244 | 2022-08-10,0.069119,0.071309,0.067389,0.071195,0.071195,431172729 245 | 2022-08-11,0.071205,0.074646,0.070727,0.070930,0.070930,563908591 246 | 2022-08-12,0.070931,0.072480,0.070027,0.072345,0.072345,320410367 247 | 2022-08-13,0.072344,0.074860,0.071951,0.072840,0.072840,446287445 248 | 2022-08-14,0.072856,0.084013,0.072754,0.081687,0.081687,1530403447 249 | 2022-08-15,0.081698,0.081820,0.075151,0.076720,0.076720,855017472 250 | 2022-08-16,0.076695,0.088639,0.076394,0.086964,0.086964,1947377918 251 | 2022-08-17,0.086933,0.088113,0.079927,0.080154,0.080154,996272861 252 | 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2022-11-17,0.085594,0.086954,0.083178,0.084765,0.084765,461654197 344 | 2022-11-18,0.084764,0.086504,0.083805,0.084672,0.084672,337485766 345 | 2022-11-19,0.084672,0.084905,0.083387,0.084581,0.084581,246661551 346 | 2022-11-20,0.084582,0.085682,0.077043,0.077174,0.077174,499031478 347 | 2022-11-21,0.077175,0.077188,0.072404,0.074711,0.074711,660061266 348 | 2022-11-22,0.074708,0.078963,0.073407,0.078488,0.078488,556392479 349 | 2022-11-23,0.078500,0.083128,0.077966,0.081786,0.081786,529744394 350 | 2022-11-24,0.081792,0.083666,0.080759,0.081481,0.081481,360326829 351 | 2022-11-25,0.081469,0.090990,0.080937,0.089475,0.089475,1022484210 352 | 2022-11-26,0.089465,0.094740,0.088391,0.088988,0.088988,902063657 353 | 2022-11-27,0.088979,0.106433,0.088979,0.098712,0.098712,1965264746 354 | 2022-11-28,0.098676,0.099327,0.091449,0.095122,0.095122,1375600313 355 | 2022-11-29,0.095131,0.104712,0.094019,0.101853,0.101853,1352427444 356 | 2022-11-30,0.101827,0.108942,0.100304,0.106860,0.106860,1388624554 357 | 2022-12-01,0.106857,0.107381,0.101071,0.101584,0.101584,891639200 358 | 2022-12-02,0.101594,0.103260,0.097639,0.102148,0.102148,776013992 359 | 2022-12-03,0.102150,0.102823,0.098651,0.099824,0.099824,632114946 360 | 2022-12-04,0.099811,0.105264,0.099806,0.104286,0.104286,759219556 361 | 2022-12-05,0.104252,0.111012,0.100063,0.101474,0.101474,1051327257 362 | 2022-12-06,0.101480,0.102627,0.098508,0.100278,0.100278,534648134 363 | 2022-12-07,0.100281,0.100950,0.095017,0.095783,0.095783,622336382 364 | 2022-12-08,0.095785,0.098874,0.095367,0.098540,0.098540,492909327 365 | 2022-12-09,0.098538,0.099086,0.096001,0.096662,0.096662,423432535 366 | 2022-12-10,0.096663,0.097356,0.096258,0.096457,0.096457,242190274 367 | 2022-12-11,0.096491,0.097070,0.096427,0.096935,0.096935,208799680 -------------------------------------------------------------------------------- /Extract Keywords using Python/Extract_Keywords.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# Extract Keywords using Python" 8 | ] 9 | }, 10 | { 11 | "cell_type": "markdown", 12 | "metadata": {}, 13 | "source": [ 14 | "Keywords play an important role when reading a long text to understand the subject and context of the text. Search engines also analyze an article’s keywords before indexing it. In this article, I will walk you through how to extract keywords using Python." 15 | ] 16 | }, 17 | { 18 | "cell_type": "markdown", 19 | "metadata": {}, 20 | "source": [ 21 | "Well, we can also train a machine learning model that will extract keywords, but here I am just going to walk you through how to use a Python library for this task so that even beginners can understand how extracting keywords work before training a machine learning model." 22 | ] 23 | }, 24 | { 25 | "cell_type": "markdown", 26 | "metadata": {}, 27 | "source": [ 28 | "# Extract Keywords using Python\n" 29 | ] 30 | }, 31 | { 32 | "cell_type": "markdown", 33 | "metadata": {}, 34 | "source": [ 35 | "There are so many Python libraries for the task of extracting keywords, the best ones are spaCy, Rake-Nltk, YAKE. In this tutorial, I will use the Rake-NLTK as it is beginner-friendly and easy to install. You can easily install it by using the pip command; pip install rake-nltk." 36 | ] 37 | }, 38 | { 39 | "cell_type": "markdown", 40 | "metadata": {}, 41 | "source": [ 42 | "RAKE stands for Rapid Automatic Keyword Extraction. It is only built to extract keywords by using the NLTK library in Python. Now let’s see how to use this library for extracting keywords." 43 | ] 44 | }, 45 | { 46 | "cell_type": "markdown", 47 | "metadata": {}, 48 | "source": [ 49 | "I will first start with importing the Rake module from the rake-nltk library" 50 | ] 51 | }, 52 | { 53 | "cell_type": "code", 54 | "execution_count": 1, 55 | "metadata": {}, 56 | "outputs": [], 57 | "source": [ 58 | "from rake_nltk import Rake\n", 59 | "rake_nltk_var = Rake()" 60 | ] 61 | }, 62 | { 63 | "cell_type": "markdown", 64 | "metadata": {}, 65 | "source": [ 66 | "Now I will store some text into a variable" 67 | ] 68 | }, 69 | { 70 | "cell_type": "code", 71 | "execution_count": 6, 72 | "metadata": {}, 73 | "outputs": [], 74 | "source": [ 75 | "text = \"\"\" I am a programmer from Bangladesh, and I am here to guide you \n", 76 | "with Data Science, Machine Learning, Python, and C++ for free. \n", 77 | "I hope you will learn a lot in your journey towards Coding, \n", 78 | "Machine Learning and Artificial Intelligence with me.\"\"\"" 79 | ] 80 | }, 81 | { 82 | "cell_type": "markdown", 83 | "metadata": {}, 84 | "source": [ 85 | "Now let’s extract the keywords from the text and print the output" 86 | ] 87 | }, 88 | { 89 | "cell_type": "code", 90 | "execution_count": 7, 91 | "metadata": {}, 92 | "outputs": [ 93 | { 94 | "name": "stdout", 95 | "output_type": "stream", 96 | "text": [ 97 | "['journey towards coding', 'machine learning', 'machine learning', 'data science', 'c ++', 'artificial intelligence', 'python', 'programmer', 'lot', 'learn', 'hope', 'guide', 'free', 'bangladesh']\n" 98 | ] 99 | } 100 | ], 101 | "source": [ 102 | "rake_nltk_var.extract_keywords_from_text(text)\n", 103 | "keyword_extracted = rake_nltk_var.get_ranked_phrases()\n", 104 | "print(keyword_extracted)" 105 | ] 106 | }, 107 | { 108 | "cell_type": "markdown", 109 | "metadata": {}, 110 | "source": [ 111 | "# Summary" 112 | ] 113 | }, 114 | { 115 | "cell_type": "markdown", 116 | "metadata": {}, 117 | "source": [ 118 | "The process of extracting keywords helps us identifying the importance of words in a text. This task can be also used for topic modelling. It is very useful to extract keywords for indexing the articles on the web so that people searching the keywords can get the best articles to read." 119 | ] 120 | }, 121 | { 122 | "cell_type": "markdown", 123 | "metadata": {}, 124 | "source": [ 125 | "This technique is also used by various search engines. It is obvious that they don’t use any library but the process remains the same to extract keywords. " 126 | ] 127 | }, 128 | { 129 | "cell_type": "markdown", 130 | "metadata": {}, 131 | "source": [ 132 | "# Sheikh Rasel Ahmed" 133 | ] 134 | }, 135 | { 136 | "cell_type": "markdown", 137 | "metadata": {}, 138 | "source": [ 139 | "##### Data Science || Machine Learning || Deep Learning || Artificial Intelligence Enthusiast" 140 | ] 141 | }, 142 | { 143 | "cell_type": "markdown", 144 | "metadata": {}, 145 | "source": [ 146 | "##### LinkedIn - https://www.linkedin.com/in/shekhnirob1\n", 147 | "##### GitHub - https://github.com/Rasel1435\n", 148 | "##### Behance - https://www.behance.net/Shekhrasel2513" 149 | ] 150 | } 151 | ], 152 | "metadata": { 153 | "kernelspec": { 154 | "display_name": "Python 3.10.5 64-bit", 155 | "language": "python", 156 | "name": "python3" 157 | }, 158 | "language_info": { 159 | "codemirror_mode": { 160 | "name": "ipython", 161 | "version": 3 162 | }, 163 | "file_extension": ".py", 164 | "mimetype": "text/x-python", 165 | "name": "python", 166 | "nbconvert_exporter": "python", 167 | "pygments_lexer": "ipython3", 168 | "version": "3.10.5" 169 | }, 170 | "orig_nbformat": 4, 171 | "vscode": { 172 | "interpreter": { 173 | "hash": "afb734500600fd355917ca529030176ea0ca205570884b88f2f6f7d791fd3fbe" 174 | } 175 | } 176 | }, 177 | "nbformat": 4, 178 | "nbformat_minor": 2 179 | } 180 | -------------------------------------------------------------------------------- /Financial Budget Analysis with Python/data/india_2021_financial_budget.csv: -------------------------------------------------------------------------------- 1 | Department /Ministry,Fund allotted(in ₹crores) 2 | MINISTRY OF AGRICULTURE,131531.19 3 | DEPARTMENT OF ATOMIC ENERGY,18264.89 4 | "MINISTRY OF AYURVEDA, YOGA",2970.3 5 | MINISTRY OF CHEMICALS AND FERTILISER,80714.94 6 | MINISTRY OF CIVIL AVIATION,3224.67 7 | MINISTRY OF COAL,534.88 8 | MINISTRY OF COMMERCE AND INDUSTRY,12768.25 9 | MINISTRY OF COMMUNICATION,75265.22 10 | MINISTRY OF CONSUMER AFFAIRS,256948.4 11 | MINISTRY OF CORPORATE AFFAIRS,712.13 12 | MINISTRY OF CULTURE,2687.99 13 | MINISTRY OF DEFENCE,478195.62 14 | MINISTRY OF DEVELOPMENT OF NORTH EASTERN REGION,2658 15 | MINISTRY OF EARTH SCIENCES,1897.13 16 | MINISTRY OF EDUCATION,93224.31 17 | MINISTRY OF ELECTRONICS AND INFORMATION TECHNOLOGY,9720.66 18 | "MINISTRY OF ENVIRONMENT, FOREST",2869.93 19 | MINISTRY OF EXTERNAL AFFAIRS,18154.73 20 | MINISTRY OF FINANCE,1386273.3 21 | "MINISTRY OF FISHERIES, ANIMAL HUSBANDRY",4322.82 22 | MINISTRY OF FOOD PROCESSING INDUSTRIES,1308.66 23 | MINISTRY OF HEALTH AND FAMILY WELFARE,73931.77 24 | MINISTRY OF HEAVY INDUSTRIES,1017.08 25 | MINISTRY OF HOME AFFAIRS,166546.94 26 | MINISTRY OF HOUSING AND URBAN AFFAIRS,54581 27 | MINISTRY OF INFORMATION AND BROADCASTING,4071.23 28 | MINISTRY OF JAL SHAKTI,69053.02 29 | MINISTRY OF LABOUR AND EMPLOYMENT,13306.5 30 | MINISTRY OF LAW AND JUSTICE,3229.94 31 | "MINISTRY OF MICRO, SMALL AND MEDIUM ENTERPRISES",15699.65 32 | MINISTRY OF MINES,1466.82 33 | MINISTRY OF MINORITY AFFAIR,4810.77 34 | MINISTRY OF NEW AND RENEWABLE ENERGY,5753 35 | MINISTRY OF PANCHAYATI RAJ,913.43 36 | MINISTRY OF PARLIAMENTARY AFFAIRS,65.07 37 | "MINISTRY OF PERSONNEL, PUBLIC GRIEVANCES",2097.24 38 | MINISTRY OF PETROLEUM AND NATURAL GAS,15943.78 39 | MINISTRY OF PLANNING,1062.77 40 | "MINISTRY OF PORTS, SHIPPING",1702.35 41 | MINISTRY OF POWER,15322 42 | "THE PRESIDENT, PARLIAMENT, UNION PUBLIC SERVICE COMMISSION",1687.57 43 | MINISTRY OF RAILWAYS,110054.64 44 | MINISTRY OF ROAD TRANSPORT AND HIGHWAY,118101 45 | MINISTRY OF RURAL DEVELOPMENT,133689.5 46 | MINISTRY OF SCIENCE AND TECHNOLOGY,14794.03 47 | MINISTRY OF SKILL DEVELOPMENT,2785.23 48 | MINISTRY OF SOCIAL JUSTICE AND EMPOWERMENT,11689.39 49 | DEPARMENT OF SPACE,13949.09 50 | MINISTRY OF STATISTICS,1409.13 51 | MINISTRY OF STEEL,39.25 52 | MINISTRY OF TEXTILES,3631.64 53 | MINISTRY OF TOURISM,2026.77 54 | MINISTRY OF TRIBAL AFFAIRS,7524.87 55 | MINISTRY OF WOMEN AND CHILD DEVELOPMENT,24435 56 | MINISTRY OF YOUTH AFFAIRS AND SPORTS,2596.14 57 | , 58 | GRAND TOTAL,3483235.63 -------------------------------------------------------------------------------- /Future Sales Prediction with Machine Learning/data/salse.csv: -------------------------------------------------------------------------------- 1 | TV,Radio,Newspaper,Sales 2 | 230.1,37.8,69.2,22.1 3 | 44.5,39.3,45.1,10.4 4 | 17.2,45.9,69.3,12 5 | 151.5,41.3,58.5,16.5 6 | 180.8,10.8,58.4,17.9 7 | 8.7,48.9,75,7.2 8 | 57.5,32.8,23.5,11.8 9 | 120.2,19.6,11.6,13.2 10 | 8.6,2.1,1,4.8 11 | 199.8,2.6,21.2,15.6 12 | 66.1,5.8,24.2,12.6 13 | 214.7,24,4,17.4 14 | 23.8,35.1,65.9,9.2 15 | 97.5,7.6,7.2,13.7 16 | 204.1,32.9,46,19 17 | 195.4,47.7,52.9,22.4 18 | 67.8,36.6,114,12.5 19 | 281.4,39.6,55.8,24.4 20 | 69.2,20.5,18.3,11.3 21 | 147.3,23.9,19.1,14.6 22 | 218.4,27.7,53.4,18 23 | 237.4,5.1,23.5,17.5 24 | 13.2,15.9,49.6,5.6 25 | 228.3,16.9,26.2,20.5 26 | 62.3,12.6,18.3,9.7 27 | 262.9,3.5,19.5,17 28 | 142.9,29.3,12.6,15 29 | 240.1,16.7,22.9,20.9 30 | 248.8,27.1,22.9,18.9 31 | 70.6,16,40.8,10.5 32 | 292.9,28.3,43.2,21.4 33 | 112.9,17.4,38.6,11.9 34 | 97.2,1.5,30,13.2 35 | 265.6,20,0.3,17.4 36 | 95.7,1.4,7.4,11.9 37 | 290.7,4.1,8.5,17.8 38 | 266.9,43.8,5,25.4 39 | 74.7,49.4,45.7,14.7 40 | 43.1,26.7,35.1,10.1 41 | 228,37.7,32,21.5 42 | 202.5,22.3,31.6,16.6 43 | 177,33.4,38.7,17.1 44 | 293.6,27.7,1.8,20.7 45 | 206.9,8.4,26.4,17.9 46 | 25.1,25.7,43.3,8.5 47 | 175.1,22.5,31.5,16.1 48 | 89.7,9.9,35.7,10.6 49 | 239.9,41.5,18.5,23.2 50 | 227.2,15.8,49.9,19.8 51 | 66.9,11.7,36.8,9.7 52 | 199.8,3.1,34.6,16.4 53 | 100.4,9.6,3.6,10.7 54 | 216.4,41.7,39.6,22.6 55 | 182.6,46.2,58.7,21.2 56 | 262.7,28.8,15.9,20.2 57 | 198.9,49.4,60,23.7 58 | 7.3,28.1,41.4,5.5 59 | 136.2,19.2,16.6,13.2 60 | 210.8,49.6,37.7,23.8 61 | 210.7,29.5,9.3,18.4 62 | 53.5,2,21.4,8.1 63 | 261.3,42.7,54.7,24.2 64 | 239.3,15.5,27.3,20.7 65 | 102.7,29.6,8.4,14 66 | 131.1,42.8,28.9,16 67 | 69,9.3,0.9,11.3 68 | 31.5,24.6,2.2,11 69 | 139.3,14.5,10.2,13.4 70 | 237.4,27.5,11,18.9 71 | 216.8,43.9,27.2,22.3 72 | 199.1,30.6,38.7,18.3 73 | 109.8,14.3,31.7,12.4 74 | 26.8,33,19.3,8.8 75 | 129.4,5.7,31.3,11 76 | 213.4,24.6,13.1,17 77 | 16.9,43.7,89.4,8.7 78 | 27.5,1.6,20.7,6.9 79 | 120.5,28.5,14.2,14.2 80 | 5.4,29.9,9.4,5.3 81 | 116,7.7,23.1,11 82 | 76.4,26.7,22.3,11.8 83 | 239.8,4.1,36.9,17.3 84 | 75.3,20.3,32.5,11.3 85 | 68.4,44.5,35.6,13.6 86 | 213.5,43,33.8,21.7 87 | 193.2,18.4,65.7,20.2 88 | 76.3,27.5,16,12 89 | 110.7,40.6,63.2,16 90 | 88.3,25.5,73.4,12.9 91 | 109.8,47.8,51.4,16.7 92 | 134.3,4.9,9.3,14 93 | 28.6,1.5,33,7.3 94 | 217.7,33.5,59,19.4 95 | 250.9,36.5,72.3,22.2 96 | 107.4,14,10.9,11.5 97 | 163.3,31.6,52.9,16.9 98 | 197.6,3.5,5.9,16.7 99 | 184.9,21,22,20.5 100 | 289.7,42.3,51.2,25.4 101 | 135.2,41.7,45.9,17.2 102 | 222.4,4.3,49.8,16.7 103 | 296.4,36.3,100.9,23.8 104 | 280.2,10.1,21.4,19.8 105 | 187.9,17.2,17.9,19.7 106 | 238.2,34.3,5.3,20.7 107 | 137.9,46.4,59,15 108 | 25,11,29.7,7.2 109 | 90.4,0.3,23.2,12 110 | 13.1,0.4,25.6,5.3 111 | 255.4,26.9,5.5,19.8 112 | 225.8,8.2,56.5,18.4 113 | 241.7,38,23.2,21.8 114 | 175.7,15.4,2.4,17.1 115 | 209.6,20.6,10.7,20.9 116 | 78.2,46.8,34.5,14.6 117 | 75.1,35,52.7,12.6 118 | 139.2,14.3,25.6,12.2 119 | 76.4,0.8,14.8,9.4 120 | 125.7,36.9,79.2,15.9 121 | 19.4,16,22.3,6.6 122 | 141.3,26.8,46.2,15.5 123 | 18.8,21.7,50.4,7 124 | 224,2.4,15.6,16.6 125 | 123.1,34.6,12.4,15.2 126 | 229.5,32.3,74.2,19.7 127 | 87.2,11.8,25.9,10.6 128 | 7.8,38.9,50.6,6.6 129 | 80.2,0,9.2,11.9 130 | 220.3,49,3.2,24.7 131 | 59.6,12,43.1,9.7 132 | 0.7,39.6,8.7,1.6 133 | 265.2,2.9,43,17.7 134 | 8.4,27.2,2.1,5.7 135 | 219.8,33.5,45.1,19.6 136 | 36.9,38.6,65.6,10.8 137 | 48.3,47,8.5,11.6 138 | 25.6,39,9.3,9.5 139 | 273.7,28.9,59.7,20.8 140 | 43,25.9,20.5,9.6 141 | 184.9,43.9,1.7,20.7 142 | 73.4,17,12.9,10.9 143 | 193.7,35.4,75.6,19.2 144 | 220.5,33.2,37.9,20.1 145 | 104.6,5.7,34.4,10.4 146 | 96.2,14.8,38.9,12.3 147 | 140.3,1.9,9,10.3 148 | 240.1,7.3,8.7,18.2 149 | 243.2,49,44.3,25.4 150 | 38,40.3,11.9,10.9 151 | 44.7,25.8,20.6,10.1 152 | 280.7,13.9,37,16.1 153 | 121,8.4,48.7,11.6 154 | 197.6,23.3,14.2,16.6 155 | 171.3,39.7,37.7,16 156 | 187.8,21.1,9.5,20.6 157 | 4.1,11.6,5.7,3.2 158 | 93.9,43.5,50.5,15.3 159 | 149.8,1.3,24.3,10.1 160 | 11.7,36.9,45.2,7.3 161 | 131.7,18.4,34.6,12.9 162 | 172.5,18.1,30.7,16.4 163 | 85.7,35.8,49.3,13.3 164 | 188.4,18.1,25.6,19.9 165 | 163.5,36.8,7.4,18 166 | 117.2,14.7,5.4,11.9 167 | 234.5,3.4,84.8,16.9 168 | 17.9,37.6,21.6,8 169 | 206.8,5.2,19.4,17.2 170 | 215.4,23.6,57.6,17.1 171 | 284.3,10.6,6.4,20 172 | 50,11.6,18.4,8.4 173 | 164.5,20.9,47.4,17.5 174 | 19.6,20.1,17,7.6 175 | 168.4,7.1,12.8,16.7 176 | 222.4,3.4,13.1,16.5 177 | 276.9,48.9,41.8,27 178 | 248.4,30.2,20.3,20.2 179 | 170.2,7.8,35.2,16.7 180 | 276.7,2.3,23.7,16.8 181 | 165.6,10,17.6,17.6 182 | 156.6,2.6,8.3,15.5 183 | 218.5,5.4,27.4,17.2 184 | 56.2,5.7,29.7,8.7 185 | 287.6,43,71.8,26.2 186 | 253.8,21.3,30,17.6 187 | 205,45.1,19.6,22.6 188 | 139.5,2.1,26.6,10.3 189 | 191.1,28.7,18.2,17.3 190 | 286,13.9,3.7,20.9 191 | 18.7,12.1,23.4,6.7 192 | 39.5,41.1,5.8,10.8 193 | 75.5,10.8,6,11.9 194 | 17.2,4.1,31.6,5.9 195 | 166.8,42,3.6,19.6 196 | 149.7,35.6,6,17.3 197 | 38.2,3.7,13.8,7.6 198 | 94.2,4.9,8.1,14 199 | 177,9.3,6.4,14.8 200 | 283.6,42,66.2,25.5 201 | 232.1,8.6,8.7,18.4 -------------------------------------------------------------------------------- /Future Sales Prediction with Machine Learning/newplot.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Rasel1435/Machine-Learning-Projects-with-Python-for-Beginners/60231f07b76f0fe200226cc383857c22d2fed571/Future Sales Prediction with Machine Learning/newplot.png -------------------------------------------------------------------------------- /Google Search Analysis with Python/data/output.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Rasel1435/Machine-Learning-Projects-with-Python-for-Beginners/60231f07b76f0fe200226cc383857c22d2fed571/Google Search Analysis with Python/data/output.png -------------------------------------------------------------------------------- /Instagram Reach Analysis and Prediction/output.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Rasel1435/Machine-Learning-Projects-with-Python-for-Beginners/60231f07b76f0fe200226cc383857c22d2fed571/Instagram Reach Analysis and Prediction/output.png -------------------------------------------------------------------------------- /Language Detection with Python/Language Detection.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# Language Detection with Python" 8 | ] 9 | }, 10 | { 11 | "cell_type": "markdown", 12 | "metadata": {}, 13 | "source": [ 14 | "The task of language detection comes into use when you are working on a very large dataset that contains data in different languages. Suppose you want to create an even driven program where the final output depends on the language which the user provides as an input. So it is important to first detect the language of the text provided by the user before taking any action. So, in this article, I will take you through the task of language detection with Python." 15 | ] 16 | }, 17 | { 18 | "cell_type": "markdown", 19 | "metadata": {}, 20 | "source": [ 21 | "As an open-source programming language, Python provides libraries and packages for almost every possible task, as the Python programming community continues to contribute to Python with new libraries, packages, and modules. You can build your machine learning model for the language detection task, but for this article, I will be using the langdetect package in Python which can detect over 55 different languages within a few lines of code." 22 | ] 23 | }, 24 | { 25 | "cell_type": "markdown", 26 | "metadata": {}, 27 | "source": [ 28 | "If you have never used this package before then you can easily install it by using the pip command; pip install langdetect. Now let’s see how to use this package for the task of language detection with Python:" 29 | ] 30 | }, 31 | { 32 | "cell_type": "code", 33 | "execution_count": 3, 34 | "metadata": {}, 35 | "outputs": [ 36 | { 37 | "name": "stdout", 38 | "output_type": "stream", 39 | "text": [ 40 | "This is input from User: আমার নাম শেখ রাসেল আহমেদ\n", 41 | "Language: bn\n" 42 | ] 43 | } 44 | ], 45 | "source": [ 46 | "from langdetect import detect\n", 47 | "text = input(\"Enter any text in any language: \")\n", 48 | "print(f\"This is input from User: {text}\")\n", 49 | "print(f\"Language: {detect(text)}\")" 50 | ] 51 | }, 52 | { 53 | "cell_type": "markdown", 54 | "metadata": {}, 55 | "source": [ 56 | "In the above code, I started by importing the detect method from the langdetect package. Then, I am simply asking for user input where the user can enter text in any language. Then I am simply printing the language of the text entered by the user by detecting it using the detect method." 57 | ] 58 | }, 59 | { 60 | "cell_type": "markdown", 61 | "metadata": {}, 62 | "source": [ 63 | "# Summary" 64 | ] 65 | }, 66 | { 67 | "cell_type": "markdown", 68 | "metadata": {}, 69 | "source": [ 70 | "The output received is an abbreviated form of the language (bn means Bangla). So the user can enter text in whatever language the user likes, your program will detect that language but it will give the short form of that language as output. Hope you liked this article on the language detection task with Python. Please feel free to ask your valuable questions in the comments section below." 71 | ] 72 | }, 73 | { 74 | "cell_type": "markdown", 75 | "metadata": {}, 76 | "source": [ 77 | "# Sheikh Rasel Ahmed" 78 | ] 79 | }, 80 | { 81 | "cell_type": "markdown", 82 | "metadata": {}, 83 | "source": [ 84 | "##### Data Science || Machine Learning || Deep Learning || Artificial Intelligence Enthusiast" 85 | ] 86 | }, 87 | { 88 | "cell_type": "markdown", 89 | "metadata": {}, 90 | "source": [ 91 | "###### LinkedIn - https://www.linkedin.com/in/shekhnirob1\n", 92 | "###### GitHub - https://github.com/Rasel1435\n", 93 | "###### Behance - https://www.behance.net/Shekhrasel2513" 94 | ] 95 | } 96 | ], 97 | "metadata": { 98 | "kernelspec": { 99 | "display_name": "Python 3.10.5 64-bit", 100 | "language": "python", 101 | "name": "python3" 102 | }, 103 | "language_info": { 104 | "codemirror_mode": { 105 | "name": "ipython", 106 | "version": 3 107 | }, 108 | "file_extension": ".py", 109 | "mimetype": "text/x-python", 110 | "name": "python", 111 | "nbconvert_exporter": "python", 112 | "pygments_lexer": "ipython3", 113 | "version": "3.10.5" 114 | }, 115 | "orig_nbformat": 4, 116 | "vscode": { 117 | "interpreter": { 118 | "hash": "afb734500600fd355917ca529030176ea0ca205570884b88f2f6f7d791fd3fbe" 119 | } 120 | } 121 | }, 122 | "nbformat": 4, 123 | "nbformat_minor": 2 124 | } 125 | -------------------------------------------------------------------------------- /Language Translator using Python/Language_Translator.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# Language Translator using Python" 8 | ] 9 | }, 10 | { 11 | "cell_type": "markdown", 12 | "metadata": {}, 13 | "source": [ 14 | "Language translator is one of those apps that helped us a lot for any language. Google Translator API helps us to create our translator using Python. In this article, I will walk you through how to create an interactive language translator using Python." 15 | ] 16 | }, 17 | { 18 | "cell_type": "markdown", 19 | "metadata": {}, 20 | "source": [ 21 | "You can easily translate any language using Google translator, but creating your translator will be a satisfying experience as a programmer. The googletrans API created by Google developers was used earlier to translate any text using Python. But now the new API from the Google developers known as google_trans_new is used for the same task." 22 | ] 23 | }, 24 | { 25 | "cell_type": "markdown", 26 | "metadata": {}, 27 | "source": [ 28 | "You can use this API for every task that you can do with the google translator. You can easily install this library in your systems by using the pip command; pip install google_trans_new. To make a translator more interactive I will also create an interactive user interface by using the streamlit library in Python. Now let’s see how to create an interactive language translator using Python:" 29 | ] 30 | }, 31 | { 32 | "cell_type": "markdown", 33 | "metadata": {}, 34 | "source": [ 35 | "#### pip install google_trans_new\n", 36 | "#### pip install streamlit" 37 | ] 38 | }, 39 | { 40 | "cell_type": "code", 41 | "execution_count": 3, 42 | "metadata": {}, 43 | "outputs": [], 44 | "source": [ 45 | "from google_trans_new import google_translator\n", 46 | "import streamlit as st\n", 47 | "translator = google_translator()\n", 48 | "st.title(\"Language Translator\")\n", 49 | "text = st.text_input(\"Enter a text\")\n", 50 | "translate = translator.translate(text, lang_tgt='bn')\n", 51 | "st.write(translate)" 52 | ] 53 | }, 54 | { 55 | "cell_type": "markdown", 56 | "metadata": {}, 57 | "source": [ 58 | "Since we are using the streamlit library here, we need to run this code using the streamlit run file name.py command in your terminal. After running the file a link will open in your default browser with an interactive user interface. In the text field, you can enter any text in any language, once you hit enter it will translate it to French as I have mentioned ‘fr’ as our target language in the above code." 59 | ] 60 | }, 61 | { 62 | "cell_type": "markdown", 63 | "metadata": {}, 64 | "source": [ 65 | "Below is how the final output will appear:" 66 | ] 67 | }, 68 | { 69 | "cell_type": "markdown", 70 | "metadata": {}, 71 | "source": [ 72 | "# Language Translator" 73 | ] 74 | }, 75 | { 76 | "cell_type": "markdown", 77 | "metadata": {}, 78 | "source": [ 79 | "Enter a text" 80 | ] 81 | }, 82 | { 83 | "cell_type": "code", 84 | "execution_count": 4, 85 | "metadata": {}, 86 | "outputs": [], 87 | "source": [ 88 | "# I love My Country" 89 | ] 90 | }, 91 | { 92 | "cell_type": "markdown", 93 | "metadata": {}, 94 | "source": [ 95 | "আমি আমার দেশকে ভালোবাসি" 96 | ] 97 | }, 98 | { 99 | "cell_type": "markdown", 100 | "metadata": {}, 101 | "source": [ 102 | "## Summary" 103 | ] 104 | }, 105 | { 106 | "cell_type": "markdown", 107 | "metadata": {}, 108 | "source": [ 109 | "This is how we can create a language translator using Python using the Google translation API developed by Google developers. You can try this app with more languages and a more interactive user interface. Hope this article taught you how to create an interactive language translator using Python. Please feel free to ask your valuable questions in the comments section below." 110 | ] 111 | }, 112 | { 113 | "cell_type": "markdown", 114 | "metadata": {}, 115 | "source": [ 116 | "# Sheikh Rasel Ahmed" 117 | ] 118 | }, 119 | { 120 | "cell_type": "markdown", 121 | "metadata": {}, 122 | "source": [ 123 | "##### Data Science || Machine Learning || Deep Learning || Artificial Intelligence Enthusiast" 124 | ] 125 | }, 126 | { 127 | "cell_type": "markdown", 128 | "metadata": {}, 129 | "source": [ 130 | "##### LinkedIn - https://www.linkedin.com/in/shekhnirob1\n", 131 | "\n", 132 | "##### GitHub - https://github.com/Rasel1435\n", 133 | "\n", 134 | "##### Behance - https://www.behance.net/Shekhrasel2513\n" 135 | ] 136 | } 137 | ], 138 | "metadata": { 139 | "kernelspec": { 140 | "display_name": "Python 3.10.5 64-bit", 141 | "language": "python", 142 | "name": "python3" 143 | }, 144 | "language_info": { 145 | "codemirror_mode": { 146 | "name": "ipython", 147 | "version": 3 148 | }, 149 | "file_extension": ".py", 150 | "mimetype": "text/x-python", 151 | "name": "python", 152 | "nbconvert_exporter": "python", 153 | "pygments_lexer": "ipython3", 154 | "version": "3.10.5" 155 | }, 156 | "orig_nbformat": 4, 157 | "vscode": { 158 | "interpreter": { 159 | "hash": "afb734500600fd355917ca529030176ea0ca205570884b88f2f6f7d791fd3fbe" 160 | } 161 | } 162 | }, 163 | "nbformat": 4, 164 | "nbformat_minor": 2 165 | } 166 | -------------------------------------------------------------------------------- /Machine Learning Project Walkthrough with Python/data/gender_submission.csv: -------------------------------------------------------------------------------- 1 | PassengerId,Survived 2 | 892,0 3 | 893,1 4 | 894,0 5 | 895,0 6 | 896,1 7 | 897,0 8 | 898,1 9 | 899,0 10 | 900,1 11 | 901,0 12 | 902,0 13 | 903,0 14 | 904,1 15 | 905,0 16 | 906,1 17 | 907,1 18 | 908,0 19 | 909,0 20 | 910,1 21 | 911,1 22 | 912,0 23 | 913,0 24 | 914,1 25 | 915,0 26 | 916,1 27 | 917,0 28 | 918,1 29 | 919,0 30 | 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2021-07-28,288.989990,290.149994,283.829987,286.220001,283.901794,33566900 12 | 2021-07-29,286.239990,288.619995,286.079987,286.500000,284.179504,18168300 13 | 2021-07-30,285.170013,286.660004,283.910004,284.910004,282.602417,20944800 14 | 2021-08-02,286.359985,286.769989,283.739990,284.820007,282.513153,16267400 15 | 2021-08-03,285.420013,287.230011,284.000000,287.119995,284.794495,17879000 16 | 2021-08-04,286.220001,287.589996,284.649994,286.510010,284.189453,16191300 17 | 2021-08-05,286.880005,289.630005,286.100006,289.519989,287.175049,13900200 18 | 2021-08-06,288.510010,289.500000,287.619995,289.459991,287.115540,16589300 19 | 2021-08-09,289.750000,291.549988,287.809998,288.329987,285.994720,16117600 20 | 2021-08-10,288.799988,289.250000,285.200012,286.440002,284.120026,18616600 21 | 2021-08-11,287.209991,288.660004,285.859985,286.950012,284.625885,13955900 22 | 2021-08-12,286.630005,289.970001,286.339996,289.809998,287.462708,14561300 23 | 2021-08-13,289.480011,292.899994,289.299988,292.850006,290.478088,18249000 24 | 2021-08-16,293.190002,294.820007,290.019989,294.600006,292.213928,22507600 25 | 2021-08-17,292.390015,293.429993,291.079987,293.079987,290.706207,20075300 26 | 2021-08-18,292.040009,294.820007,290.269989,290.730011,288.927338,21813000 27 | 2021-08-19,288.690002,297.470001,288.640015,296.769989,294.929871,29850500 28 | 2021-08-20,299.720001,305.839996,298.059998,304.359985,302.472809,40817600 29 | 2021-08-23,303.250000,305.399994,301.850006,304.649994,302.760986,22830200 30 | 2021-08-24,305.019989,305.649994,302.000000,302.619995,300.743591,18175800 31 | 2021-08-25,304.299988,304.589996,300.420013,302.010010,300.137421,20006100 32 | 2021-08-26,300.989990,302.429993,298.950012,299.089996,297.235474,17666100 33 | 2021-08-27,298.989990,300.869995,296.829987,299.720001,297.861572,22605700 34 | 2021-08-30,301.119995,304.220001,301.059998,303.589996,301.707611,16348100 35 | 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2021-09-17,304.170013,304.500000,299.529999,299.869995,298.010651,41372500 48 | 2021-09-20,296.329987,298.720001,289.519989,294.299988,292.475159,38278700 49 | 2021-09-21,295.690002,297.540009,294.070007,294.799988,292.972076,22364100 50 | 2021-09-22,296.730011,300.220001,294.510010,298.579987,296.728638,26626300 51 | 2021-09-23,298.850006,300.899994,297.529999,299.559998,297.702545,18604600 52 | 2021-09-24,298.230011,299.799988,296.929993,299.350006,297.493866,14999000 53 | 2021-09-27,296.140015,296.470001,292.940002,294.170013,292.346008,23571700 54 | 2021-09-28,289.799988,290.779999,282.750000,283.519989,281.762024,43186200 55 | 2021-09-29,285.100006,286.769989,283.010010,284.000000,282.239075,26353700 56 | 2021-09-30,285.709991,287.829987,281.619995,281.920013,280.171967,32343600 57 | 2021-10-01,282.119995,289.980011,281.290009,289.100006,287.307434,30086300 58 | 2021-10-04,287.399994,287.750000,280.250000,283.109985,281.354553,31350700 59 | 2021-10-05,284.049988,290.399994,284.049988,288.760010,286.969543,24993000 60 | 2021-10-06,285.779999,293.630005,285.510010,293.109985,291.292572,28002600 61 | 2021-10-07,295.179993,296.640015,293.920013,294.850006,293.021820,20430500 62 | 2021-10-08,296.220001,296.640015,293.760010,294.850006,293.021820,17685700 63 | 2021-10-11,292.920013,297.970001,292.750000,294.230011,292.405670,19298600 64 | 2021-10-12,295.339996,295.440002,292.350006,292.880005,291.063995,17974100 65 | 2021-10-13,294.910004,297.279999,293.489990,296.309998,294.472717,23416300 66 | 2021-10-14,299.209991,303.269989,297.829987,302.750000,300.872772,27262900 67 | 2021-10-15,302.339996,304.450012,300.519989,304.209991,302.323761,25384800 68 | 2021-10-18,303.570007,308.209991,302.690002,307.290009,305.384674,22729300 69 | 2021-10-19,308.350006,309.299988,307.220001,308.230011,306.318848,17682100 70 | 2021-10-20,309.209991,309.700012,306.109985,307.410004,305.503906,16537100 71 | 2021-10-21,307.170013,311.019989,306.359985,310.760010,308.833160,16918100 72 | 2021-10-22,310.399994,311.089996,307.799988,309.160004,307.243042,17449300 73 | 2021-10-25,309.359985,309.399994,306.459991,308.130005,306.219452,17554500 74 | 2021-10-26,311.000000,312.399994,308.600006,310.109985,308.187134,28107300 75 | 2021-10-27,316.000000,326.100006,316.000000,323.170013,321.166199,52588700 76 | 2021-10-28,324.329987,324.869995,321.359985,324.350006,322.338867,26297900 77 | 2021-10-29,324.130005,332.000000,323.899994,331.619995,329.563782,34766000 78 | 2021-11-01,331.359985,331.489990,326.369995,329.369995,327.327728,27073200 79 | 2021-11-02,330.309998,333.450012,330.000000,333.130005,331.064423,26487100 80 | 2021-11-03,333.899994,334.899994,330.649994,334.000000,331.929047,21500100 81 | 2021-11-04,332.890015,336.540009,329.510010,336.440002,334.353912,23992200 82 | 2021-11-05,338.510010,338.790009,334.420013,336.059998,333.976257,22570100 83 | 2021-11-08,337.299988,337.649994,334.440002,336.989990,334.900452,20897000 84 | 2021-11-09,337.109985,338.720001,334.529999,335.950012,333.866943,21307400 85 | 2021-11-10,334.570007,334.630005,329.920013,330.799988,328.748871,25500900 86 | 2021-11-11,331.250000,333.769989,330.510010,332.429993,330.368744,16849800 87 | 2021-11-12,333.920013,337.230011,333.790009,336.720001,334.632172,23831000 88 | 2021-11-15,337.540009,337.880005,334.029999,336.070007,333.986206,16723000 89 | 2021-11-16,335.679993,340.670013,335.510010,339.510010,337.404877,20886800 90 | 2021-11-17,338.940002,342.190002,338.000000,339.119995,337.633850,19053400 91 | 2021-11-18,338.179993,342.450012,337.119995,341.269989,339.774414,22463500 92 | 2021-11-19,342.640015,345.100006,342.200012,343.109985,341.606384,21963400 93 | 2021-11-22,344.619995,349.670013,339.549988,339.829987,338.340729,31031100 94 | 2021-11-23,337.049988,339.450012,333.559998,337.679993,336.200165,30427600 95 | 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2021-12-13,340.679993,343.790009,339.079987,339.399994,337.912628,28899400 108 | 2021-12-14,333.220001,334.640015,324.109985,328.339996,326.901093,44438700 109 | 2021-12-15,328.609985,335.190002,324.500000,334.649994,333.183441,35381100 110 | 2021-12-16,335.709991,336.760010,323.019989,324.899994,323.476166,35034800 111 | 2021-12-17,320.880005,324.920013,317.250000,323.799988,322.380981,47750300 112 | 2021-12-20,320.049988,322.799988,317.570007,319.910004,318.508026,28326500 113 | 2021-12-21,323.290009,327.730011,319.799988,327.290009,325.855713,24740600 114 | 2021-12-22,328.299988,333.609985,325.750000,333.200012,331.739838,24831500 115 | 2021-12-23,332.750000,336.390015,332.730011,334.690002,333.223267,19617800 116 | 2021-12-27,335.459991,342.480011,335.429993,342.450012,340.949280,19947000 117 | 2021-12-28,343.149994,343.809998,340.320007,341.250000,339.754517,15661500 118 | 2021-12-29,341.299988,344.299988,339.679993,341.950012,340.451477,15042000 119 | 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2022-01-18,304.070007,309.799988,301.739990,302.649994,301.323669,42333200 132 | 2022-01-19,306.290009,313.910004,302.700012,303.329987,302.000671,45933900 133 | 2022-01-20,309.070007,311.649994,301.140015,301.600006,300.278290,35380700 134 | 2022-01-21,302.690002,304.109985,295.609985,296.029999,294.732697,57984400 135 | 2022-01-24,292.200012,297.109985,276.049988,296.369995,295.071198,85731500 136 | 2022-01-25,291.519989,294.989990,285.170013,288.489990,287.225739,72848600 137 | 2022-01-26,307.989990,308.500000,293.029999,296.709991,295.409698,90428900 138 | 2022-01-27,302.660004,307.299988,297.929993,299.839996,298.526001,53481300 139 | 2022-01-28,300.230011,308.480011,294.450012,308.260010,306.909119,49743700 140 | 2022-01-31,308.950012,312.380005,306.369995,310.980011,309.617188,46444500 141 | 2022-02-01,310.410004,310.630005,305.130005,308.760010,307.406921,40950400 142 | 2022-02-02,309.630005,315.119995,308.880005,313.459991,312.086304,36636000 143 | 2022-02-03,309.489990,311.230011,299.959991,301.250000,299.929810,43730000 144 | 2022-02-04,300.209991,308.799988,299.970001,305.940002,304.599274,35096500 145 | 2022-02-07,306.170013,307.839996,299.899994,300.950012,299.631134,28533300 146 | 2022-02-08,301.250000,305.559998,299.950012,304.559998,303.225311,32421200 147 | 2022-02-09,309.869995,311.929993,307.390015,311.209991,309.846161,31284700 148 | 2022-02-10,304.040009,309.119995,300.700012,302.380005,301.054901,45386200 149 | 2022-02-11,303.190002,304.290009,294.220001,295.040009,293.747040,39175600 150 | 2022-02-14,293.769989,296.760010,291.350006,295.000000,293.707214,36359500 151 | 2022-02-15,300.010010,300.799988,297.019989,300.470001,299.153259,27058300 152 | 2022-02-16,298.369995,300.869995,293.679993,299.500000,298.804047,29982100 153 | 2022-02-17,296.359985,296.799988,290.000000,290.730011,290.054443,32461600 154 | 2022-02-18,293.049988,293.859985,286.309998,287.929993,287.260925,34264000 155 | 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2022-05-02,277.709991,284.940002,276.220001,284.470001,283.808990,35151100 204 | 2022-05-03,283.959991,284.130005,280.149994,281.779999,281.125244,25978600 205 | 2022-05-04,282.589996,290.880005,276.730011,289.980011,289.306183,33599300 206 | 2022-05-05,285.540009,286.350006,274.339996,277.350006,276.705536,43260400 207 | 2022-05-06,274.809998,279.250000,271.269989,274.730011,274.091614,37780300 208 | 2022-05-09,270.059998,272.359985,263.320007,264.579987,263.965179,47726000 209 | 2022-05-10,271.690002,273.750000,265.070007,269.500000,268.873779,39336400 210 | 2022-05-11,265.679993,271.359985,259.299988,260.549988,259.944550,48975900 211 | 2022-05-12,257.690002,259.880005,250.020004,255.350006,254.756653,51033800 212 | 2022-05-13,257.350006,263.040009,255.350006,261.119995,260.513245,34925100 213 | 2022-05-16,259.959991,265.820007,255.779999,261.500000,260.892365,32550900 214 | 2022-05-17,266.109985,268.329987,262.459991,266.820007,266.200012,28828800 215 | 2022-05-18,263.000000,263.600006,252.770004,254.080002,254.080002,31356000 216 | 2022-05-19,253.899994,257.670013,251.880005,253.139999,253.139999,32692300 217 | 2022-05-20,257.239990,258.540009,246.440002,252.559998,252.559998,39199300 218 | 2022-05-23,255.490005,261.500000,253.429993,260.649994,260.649994,33175400 219 | 2022-05-24,257.890015,261.329987,253.500000,259.619995,259.619995,29043900 220 | 2022-05-25,258.140015,264.579987,257.130005,262.519989,262.519989,28547900 221 | 2022-05-26,262.269989,267.109985,261.429993,265.899994,265.899994,25002100 222 | 2022-05-27,268.480011,273.339996,267.559998,273.239990,273.239990,26910800 223 | 2022-05-31,272.529999,274.769989,268.929993,271.869995,271.869995,37827700 224 | 2022-06-01,275.200012,277.690002,270.040009,272.420013,272.420013,25292200 225 | 2022-06-02,264.450012,274.649994,261.600006,274.579987,274.579987,44008200 226 | 2022-06-03,270.309998,273.450012,268.410004,270.019989,270.019989,28059000 227 | 2022-06-06,272.059998,274.179993,267.220001,268.750000,268.750000,22400300 228 | 2022-06-07,266.640015,273.130005,265.940002,272.500000,272.500000,22860700 229 | 2022-06-08,271.709991,273.000000,269.609985,270.410004,270.410004,17372300 230 | 2022-06-09,267.779999,272.709991,264.630005,264.790009,264.790009,26439700 231 | 2022-06-10,260.579987,260.579987,252.529999,252.990005,252.990005,31422800 232 | 2022-06-13,245.110001,249.020004,241.529999,242.259995,242.259995,46135800 233 | 2022-06-14,243.860001,245.740005,241.509995,244.490005,244.490005,28651500 234 | 2022-06-15,248.309998,255.300003,246.419998,251.759995,251.759995,33111700 235 | 2022-06-16,245.979996,247.419998,243.020004,244.970001,244.970001,33169200 236 | 2022-06-17,244.699997,250.500000,244.029999,247.649994,247.649994,43084800 237 | 2022-06-21,250.259995,254.750000,249.509995,253.740005,253.740005,29928300 238 | 2022-06-22,251.889999,257.170013,250.369995,253.130005,253.130005,25939900 239 | 2022-06-23,255.570007,259.369995,253.630005,258.859985,258.859985,25861400 240 | 2022-06-24,261.809998,267.980011,261.720001,267.700012,267.700012,33923200 241 | 2022-06-27,268.209991,268.299988,263.279999,264.890015,264.890015,24615100 242 | 2022-06-28,263.980011,266.910004,256.320007,256.480011,256.480011,27295500 243 | 2022-06-29,257.589996,261.970001,255.759995,260.260010,260.260010,20069800 244 | 2022-06-30,257.049988,259.529999,252.899994,256.829987,256.829987,31730900 245 | 2022-07-01,256.390015,259.769989,254.610001,259.579987,259.579987,22825200 246 | 2022-07-05,256.160004,262.980011,254.740005,262.850006,262.850006,22941000 247 | 2022-07-06,263.750000,267.989990,262.399994,266.209991,266.209991,23824400 248 | 2022-07-07,265.119995,269.059998,265.019989,268.399994,268.399994,20859900 249 | 2022-07-08,264.790009,268.100006,263.290009,267.660004,267.660004,19648100 250 | 2022-07-11,265.649994,266.529999,262.179993,264.510010,264.510010,19455200 251 | 2022-07-12,265.880005,265.940002,252.039993,253.669998,253.669998,35868500 252 | 2022-07-13,250.190002,253.550003,248.110001,252.720001,252.720001,29497400 253 | 2022-07-14,250.570007,255.139999,245.940002,254.080002,254.080002,25074800 254 | 2022-07-15,255.720001,260.369904,255.199997,255.690002,255.690002,17438373 -------------------------------------------------------------------------------- /Online Payments Fraud Detection using Python/newplot.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Rasel1435/Machine-Learning-Projects-with-Python-for-Beginners/60231f07b76f0fe200226cc383857c22d2fed571/Online Payments Fraud Detection using Python/newplot.png -------------------------------------------------------------------------------- /Profit Prediction with Machine Learning/data/Startups.csv: -------------------------------------------------------------------------------- 1 | R&D Spend,Administration,Marketing Spend,State,Profit 2 | 165349.2,136897.8,471784.1,New York,192261.83 3 | 162597.7,151377.59,443898.53,California,191792.06 4 | 153441.51,101145.55,407934.54,Florida,191050.39 5 | 144372.41,118671.85,383199.62,New York,182901.99 6 | 142107.34,91391.77,366168.42,Florida,166187.94 7 | 131876.9,99814.71,362861.36,New York,156991.12 8 | 134615.46,147198.87,127716.82,California,156122.51 9 | 130298.13,145530.06,323876.68,Florida,155752.6 10 | 120542.52,148718.95,311613.29,New York,152211.77 11 | 123334.88,108679.17,304981.62,California,149759.96 12 | 101913.08,110594.11,229160.95,Florida,146121.95 13 | 100671.96,91790.61,249744.55,California,144259.4 14 | 93863.75,127320.38,249839.44,Florida,141585.52 15 | 91992.39,135495.07,252664.93,California,134307.35 16 | 119943.24,156547.42,256512.92,Florida,132602.65 17 | 114523.61,122616.84,261776.23,New York,129917.04 18 | 78013.11,121597.55,264346.06,California,126992.93 19 | 94657.16,145077.58,282574.31,New York,125370.37 20 | 91749.16,114175.79,294919.57,Florida,124266.9 21 | 86419.7,153514.11,0,New York,122776.86 22 | 76253.86,113867.3,298664.47,California,118474.03 23 | 78389.47,153773.43,299737.29,New York,111313.02 24 | 73994.56,122782.75,303319.26,Florida,110352.25 25 | 67532.53,105751.03,304768.73,Florida,108733.99 26 | 77044.01,99281.34,140574.81,New York,108552.04 27 | 64664.71,139553.16,137962.62,California,107404.34 28 | 75328.87,144135.98,134050.07,Florida,105733.54 29 | 72107.6,127864.55,353183.81,New York,105008.31 30 | 66051.52,182645.56,118148.2,Florida,103282.38 31 | 65605.48,153032.06,107138.38,New York,101004.64 32 | 61994.48,115641.28,91131.24,Florida,99937.59 33 | 61136.38,152701.92,88218.23,New York,97483.56 34 | 63408.86,129219.61,46085.25,California,97427.84 35 | 55493.95,103057.49,214634.81,Florida,96778.92 36 | 46426.07,157693.92,210797.67,California,96712.8 37 | 46014.02,85047.44,205517.64,New York,96479.51 38 | 28663.76,127056.21,201126.82,Florida,90708.19 39 | 44069.95,51283.14,197029.42,California,89949.14 40 | 20229.59,65947.93,185265.1,New York,81229.06 41 | 38558.51,82982.09,174999.3,California,81005.76 42 | 28754.33,118546.05,172795.67,California,78239.91 43 | 27892.92,84710.77,164470.71,Florida,77798.83 44 | 23640.93,96189.63,148001.11,California,71498.49 45 | 15505.73,127382.3,35534.17,New York,69758.98 46 | 22177.74,154806.14,28334.72,California,65200.33 47 | 1000.23,124153.04,1903.93,New York,64926.08 48 | 1315.46,115816.21,297114.46,Florida,49490.75 49 | 0,135426.92,0,California,42559.73 50 | 542.05,51743.15,0,New York,35673.41 51 | 0,116983.8,45173.06,California,14681.4 -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Machine-Learning-Projects-with-Python-for-Beginners 2 | 3 | Author: Sheikh Rasel Ahmed 4 | 5 | 6 | # Check This all --> 7 | 8 | - GitHub: https://github.com/Rasel1435 9 | - Linkedin: https://www.linkedin.com/in/shekhnirob1 10 | - YouTube: https://www.youtube.com/@codewithsheikhrasel 11 | - Medium: https://medium.com/@shekhrasel59 12 | - Kaggle: https://www.kaggle.com/sheikhraselahmed 13 | - Upwork: https://www.upwork.com/freelancers/~01b76af87fa6e9611e 14 | - leetcode: https://leetcode.com/shekh_rasel/ 15 | - Twitter: https://twitter.com/shekh_nirob 16 | - Facebook: https://www.facebook.com/rasel1435 17 | -------------------------------------------------------------------------------- /Saving a Machine Learning Model/pickle_model: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Rasel1435/Machine-Learning-Projects-with-Python-for-Beginners/60231f07b76f0fe200226cc383857c22d2fed571/Saving a Machine Learning Model/pickle_model -------------------------------------------------------------------------------- /Spam_Detection_with_Machine_Learning/Spam Detection.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "## Spam Detection with Machine Learning" 8 | ] 9 | }, 10 | { 11 | "cell_type": "markdown", 12 | "metadata": {}, 13 | "source": [ 14 | "Detecting spam alerts in emails and messages is one of the main applications that every big tech company tries to improve for its customers. Apple’s official messaging app and Google’s Gmail are great examples of such applications where spam detection works well to protect users from spam alerts. So, if you are looking to build a spam detection system, this article is for you. In this article, I will walk you through the task of Spam Detection with Machine Learning using Python." 15 | ] 16 | }, 17 | { 18 | "cell_type": "markdown", 19 | "metadata": {}, 20 | "source": [ 21 | "## Spam Detection" 22 | ] 23 | }, 24 | { 25 | "cell_type": "markdown", 26 | "metadata": {}, 27 | "source": [ 28 | "Whenever you submit details about your email or contact number on any platform, it has become easy for those platforms to market their products by advertising them by sending emails or by sending messages directly to your contact number. This results in lots of spam alerts and notifications in your inbox. This is where the task of spam detection comes in.\n", 29 | "\n", 30 | "Spam detection means detecting spam messages or emails by understanding text content so that you can only receive notifications about messages or emails that are very important to you. If spam messages are found, they are automatically transferred to a spam folder and you are never notified of such alerts. This helps to improve the user experience, as many spam alerts can bother many users." 31 | ] 32 | }, 33 | { 34 | "cell_type": "markdown", 35 | "metadata": {}, 36 | "source": [ 37 | "## Spam Detection using Python" 38 | ] 39 | }, 40 | { 41 | "cell_type": "markdown", 42 | "metadata": {}, 43 | "source": [ 44 | "Hope you now understand what spam detection is, now let’s see how to train a machine learning model for detecting spam alerts using Python. I’ll start this task by importing the necessary Python libraries and the dataset you need for this task" 45 | ] 46 | }, 47 | { 48 | "cell_type": "code", 49 | "execution_count": 170, 50 | "metadata": {}, 51 | "outputs": [], 52 | "source": [ 53 | "import pandas as pd\n", 54 | "import numpy as np\n", 55 | "import matplotlib.pyplot as plt\n", 56 | "import seaborn as sns\n", 57 | "import cv2\n", 58 | "\n", 59 | "%matplotlib inline" 60 | ] 61 | }, 62 | { 63 | "cell_type": "markdown", 64 | "metadata": {}, 65 | "source": [ 66 | "## Data Collection" 67 | ] 68 | }, 69 | { 70 | "cell_type": "code", 71 | "execution_count": 171, 72 | "metadata": {}, 73 | "outputs": [ 74 | { 75 | "data": { 76 | "text/html": [ 77 | "
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classmessageUnnamed: 2Unnamed: 3Unnamed: 4
0hamGo until jurong point, crazy.. Available only ...NaNNaNNaN
1hamOk lar... Joking wif u oni...NaNNaNNaN
2spamFree entry in 2 a wkly comp to win FA Cup fina...NaNNaNNaN
3hamU dun say so early hor... U c already then say...NaNNaNNaN
4hamNah I don't think he goes to usf, he lives aro...NaNNaNNaN
\n", 145 | "
" 146 | ], 147 | "text/plain": [ 148 | " class message Unnamed: 2 \\\n", 149 | "0 ham Go until jurong point, crazy.. Available only ... NaN \n", 150 | "1 ham Ok lar... Joking wif u oni... NaN \n", 151 | "2 spam Free entry in 2 a wkly comp to win FA Cup fina... NaN \n", 152 | "3 ham U dun say so early hor... U c already then say... NaN \n", 153 | "4 ham Nah I don't think he goes to usf, he lives aro... NaN \n", 154 | "\n", 155 | " Unnamed: 3 Unnamed: 4 \n", 156 | "0 NaN NaN \n", 157 | "1 NaN NaN \n", 158 | "2 NaN NaN \n", 159 | "3 NaN NaN \n", 160 | "4 NaN NaN " 161 | ] 162 | }, 163 | "execution_count": 171, 164 | "metadata": {}, 165 | "output_type": "execute_result" 166 | } 167 | ], 168 | "source": [ 169 | "data = pd.read_csv(r\".\\\\data\\spam_messege.csv\", encoding=\"latin-1\")\n", 170 | "data.head()" 171 | ] 172 | }, 173 | { 174 | "cell_type": "code", 175 | "execution_count": 172, 176 | "metadata": {}, 177 | "outputs": [ 178 | { 179 | "name": "stdout", 180 | "output_type": "stream", 181 | "text": [ 182 | " class message Unnamed: 2 \\\n", 183 | "0 ham Go until jurong point, crazy.. Available only ... NaN \n", 184 | "1 ham Ok lar... Joking wif u oni... NaN \n", 185 | "2 spam Free entry in 2 a wkly comp to win FA Cup fina... NaN \n", 186 | "3 ham U dun say so early hor... U c already then say... NaN \n", 187 | "4 ham Nah I don't think he goes to usf, he lives aro... NaN \n", 188 | "\n", 189 | " Unnamed: 3 Unnamed: 4 \n", 190 | "0 NaN NaN \n", 191 | "1 NaN NaN \n", 192 | "2 NaN NaN \n", 193 | "3 NaN NaN \n", 194 | "4 NaN NaN \n" 195 | ] 196 | } 197 | ], 198 | "source": [ 199 | "print(data.head())" 200 | ] 201 | }, 202 | { 203 | "cell_type": "markdown", 204 | "metadata": {}, 205 | "source": [ 206 | "## Data Pre-Processing" 207 | ] 208 | }, 209 | { 210 | "cell_type": "code", 211 | "execution_count": 173, 212 | "metadata": {}, 213 | "outputs": [ 214 | { 215 | "name": "stdout", 216 | "output_type": "stream", 217 | "text": [ 218 | "\n", 219 | "RangeIndex: 5572 entries, 0 to 5571\n", 220 | "Data columns (total 5 columns):\n", 221 | " # Column Non-Null Count Dtype \n", 222 | "--- ------ -------------- ----- \n", 223 | " 0 class 5572 non-null object\n", 224 | " 1 message 5572 non-null object\n", 225 | " 2 Unnamed: 2 50 non-null object\n", 226 | " 3 Unnamed: 3 12 non-null object\n", 227 | " 4 Unnamed: 4 6 non-null object\n", 228 | "dtypes: object(5)\n", 229 | "memory usage: 217.8+ KB\n" 230 | ] 231 | } 232 | ], 233 | "source": [ 234 | "data.info()" 235 | ] 236 | }, 237 | { 238 | "cell_type": "code", 239 | "execution_count": 174, 240 | "metadata": {}, 241 | "outputs": [ 242 | { 243 | "data": { 244 | "text/html": [ 245 | "
\n", 246 | "\n", 259 | "\n", 260 | " \n", 261 | " \n", 262 | " \n", 263 | " \n", 264 | " \n", 265 | " \n", 266 | " \n", 267 | " \n", 268 | " \n", 269 | " \n", 270 | " \n", 271 | " \n", 272 | " \n", 273 | " \n", 274 | " \n", 275 | " \n", 276 | " \n", 277 | " \n", 278 | " \n", 279 | " \n", 280 | " \n", 281 | " \n", 282 | " \n", 283 | " \n", 284 | " \n", 285 | " \n", 286 | " \n", 287 | " \n", 288 | " \n", 289 | " \n", 290 | " \n", 291 | " \n", 292 | " \n", 293 | " \n", 294 | " \n", 295 | " \n", 296 | " \n", 297 | " \n", 298 | " \n", 299 | " \n", 300 | " \n", 301 | " \n", 302 | " \n", 303 | " \n", 304 | "
classmessageUnnamed: 2Unnamed: 3Unnamed: 4
count5572557250126
unique2516943105
tophamSorry, I'll call laterbt not his girlfrnd... G o o d n i g h t . . .@\"MK17 92H. 450Ppw 16\"GNT:-)\"
freq482530322
\n", 305 | "
" 306 | ], 307 | "text/plain": [ 308 | " class message \\\n", 309 | "count 5572 5572 \n", 310 | "unique 2 5169 \n", 311 | "top ham Sorry, I'll call later \n", 312 | "freq 4825 30 \n", 313 | "\n", 314 | " Unnamed: 2 \\\n", 315 | "count 50 \n", 316 | "unique 43 \n", 317 | "top bt not his girlfrnd... G o o d n i g h t . . .@\" \n", 318 | "freq 3 \n", 319 | "\n", 320 | " Unnamed: 3 Unnamed: 4 \n", 321 | "count 12 6 \n", 322 | "unique 10 5 \n", 323 | "top MK17 92H. 450Ppw 16\" GNT:-)\" \n", 324 | "freq 2 2 " 325 | ] 326 | }, 327 | "execution_count": 174, 328 | "metadata": {}, 329 | "output_type": "execute_result" 330 | } 331 | ], 332 | "source": [ 333 | "data.describe()" 334 | ] 335 | }, 336 | { 337 | "cell_type": "code", 338 | "execution_count": 175, 339 | "metadata": {}, 340 | "outputs": [ 341 | { 342 | "name": "stdout", 343 | "output_type": "stream", 344 | "text": [ 345 | " class message \\\n", 346 | "count 5572 5572 \n", 347 | "unique 2 5169 \n", 348 | "top ham Sorry, I'll call later \n", 349 | "freq 4825 30 \n", 350 | "\n", 351 | " Unnamed: 2 \\\n", 352 | "count 50 \n", 353 | "unique 43 \n", 354 | "top bt not his girlfrnd... G o o d n i g h t . . .@\" \n", 355 | "freq 3 \n", 356 | "\n", 357 | " Unnamed: 3 Unnamed: 4 \n", 358 | "count 12 6 \n", 359 | "unique 10 5 \n", 360 | "top MK17 92H. 450Ppw 16\" GNT:-)\" \n", 361 | "freq 2 2 \n" 362 | ] 363 | } 364 | ], 365 | "source": [ 366 | "print(data.describe())" 367 | ] 368 | }, 369 | { 370 | "cell_type": "code", 371 | "execution_count": 176, 372 | "metadata": {}, 373 | "outputs": [ 374 | { 375 | "data": { 376 | "text/plain": [ 377 | "Index(['class', 'message', 'Unnamed: 2', 'Unnamed: 3', 'Unnamed: 4'], dtype='object')" 378 | ] 379 | }, 380 | "execution_count": 176, 381 | "metadata": {}, 382 | "output_type": "execute_result" 383 | } 384 | ], 385 | "source": [ 386 | "data.columns" 387 | ] 388 | }, 389 | { 390 | "cell_type": "markdown", 391 | "metadata": {}, 392 | "source": [ 393 | "From this dataset, class and message are the only features we need to train a machine learning model for spam detection, so let’s select these two columns as the new dataset" 394 | ] 395 | }, 396 | { 397 | "cell_type": "code", 398 | "execution_count": 177, 399 | "metadata": {}, 400 | "outputs": [], 401 | "source": [ 402 | "data = data[[\"class\", \"message\"]]" 403 | ] 404 | }, 405 | { 406 | "cell_type": "code", 407 | "execution_count": 178, 408 | "metadata": {}, 409 | "outputs": [ 410 | { 411 | "data": { 412 | "text/plain": [ 413 | "class\n", 414 | "ham 4825\n", 415 | "spam 747\n", 416 | "dtype: int64" 417 | ] 418 | }, 419 | "execution_count": 178, 420 | "metadata": {}, 421 | "output_type": "execute_result" 422 | } 423 | ], 424 | "source": [ 425 | "data[[\"class\"]].value_counts()" 426 | ] 427 | }, 428 | { 429 | "cell_type": "code", 430 | "execution_count": 179, 431 | "metadata": {}, 432 | "outputs": [ 433 | { 434 | "data": { 435 | "text/plain": [ 436 | "message \n", 437 | "Sorry, I'll call later 30\n", 438 | "I cant pick the phone right now. Pls send a message 12\n", 439 | "Ok... 10\n", 440 | "Your opinion about me? 1. Over 2. Jada 3. Kusruthi 4. Lovable 5. Silent 6. Spl character 7. Not matured 8. Stylish 9. Simple Pls reply.. 4\n", 441 | "Wen ur lovable bcums angry wid u, dnt take it seriously.. Coz being angry is d most childish n true way of showing deep affection, care n luv!.. kettoda manda... Have nice day da. 4\n", 442 | "dtype: int64" 443 | ] 444 | }, 445 | "execution_count": 179, 446 | "metadata": {}, 447 | "output_type": "execute_result" 448 | } 449 | ], 450 | "source": [ 451 | "data[[\"message\"]].value_counts().head(5)" 452 | ] 453 | }, 454 | { 455 | "cell_type": "markdown", 456 | "metadata": {}, 457 | "source": [ 458 | "## Feature Selection" 459 | ] 460 | }, 461 | { 462 | "cell_type": "code", 463 | "execution_count": 180, 464 | "metadata": {}, 465 | "outputs": [], 466 | "source": [ 467 | "feature = np.array(data[\"message\"])\n", 468 | "target = np.array(data[\"class\"])" 469 | ] 470 | }, 471 | { 472 | "cell_type": "code", 473 | "execution_count": 181, 474 | "metadata": {}, 475 | "outputs": [], 476 | "source": [ 477 | "from sklearn.feature_extraction.text import CountVectorizer\n", 478 | "fem = CountVectorizer()\n", 479 | "feature = fem.fit_transform(feature)" 480 | ] 481 | }, 482 | { 483 | "cell_type": "markdown", 484 | "metadata": {}, 485 | "source": [ 486 | "## Spliting Data" 487 | ] 488 | }, 489 | { 490 | "cell_type": "code", 491 | "execution_count": 182, 492 | "metadata": {}, 493 | "outputs": [], 494 | "source": [ 495 | "from sklearn.model_selection import train_test_split\n", 496 | "xtrain, xtest, ytrain, ytest = train_test_split(feature, target, test_size=0.33,random_state=42)" 497 | ] 498 | }, 499 | { 500 | "cell_type": "code", 501 | "execution_count": 183, 502 | "metadata": {}, 503 | "outputs": [ 504 | { 505 | "data": { 506 | "text/plain": [ 507 | "((1839, 8710), (3733, 8710))" 508 | ] 509 | }, 510 | "execution_count": 183, 511 | "metadata": {}, 512 | "output_type": "execute_result" 513 | } 514 | ], 515 | "source": [ 516 | "xtest.shape, xtrain.shape" 517 | ] 518 | }, 519 | { 520 | "cell_type": "code", 521 | "execution_count": 184, 522 | "metadata": {}, 523 | "outputs": [ 524 | { 525 | "data": { 526 | "text/plain": [ 527 | "((1839,), (3733,))" 528 | ] 529 | }, 530 | "execution_count": 184, 531 | "metadata": {}, 532 | "output_type": "execute_result" 533 | } 534 | ], 535 | "source": [ 536 | "ytest.shape, ytrain.shape" 537 | ] 538 | }, 539 | { 540 | "cell_type": "markdown", 541 | "metadata": {}, 542 | "source": [ 543 | "## Choosing Model & Training The Model" 544 | ] 545 | }, 546 | { 547 | "cell_type": "code", 548 | "execution_count": 185, 549 | "metadata": {}, 550 | "outputs": [ 551 | { 552 | "data": { 553 | "text/html": [ 554 | "
MultinomialNB()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" 555 | ], 556 | "text/plain": [ 557 | "MultinomialNB()" 558 | ] 559 | }, 560 | "execution_count": 185, 561 | "metadata": {}, 562 | "output_type": "execute_result" 563 | } 564 | ], 565 | "source": [ 566 | "from sklearn.naive_bayes import MultinomialNB\n", 567 | "nbm = MultinomialNB()\n", 568 | "nbm.fit(xtrain, ytrain.ravel())" 569 | ] 570 | }, 571 | { 572 | "cell_type": "markdown", 573 | "metadata": {}, 574 | "source": [ 575 | "## Predicting Test Data" 576 | ] 577 | }, 578 | { 579 | "cell_type": "code", 580 | "execution_count": 186, 581 | "metadata": {}, 582 | "outputs": [], 583 | "source": [ 584 | "predictions = nbm.predict(xtest)" 585 | ] 586 | }, 587 | { 588 | "cell_type": "code", 589 | "execution_count": 187, 590 | "metadata": {}, 591 | "outputs": [ 592 | { 593 | "data": { 594 | "text/plain": [ 595 | "array(['spam', 'ham', 'spam', ..., 'ham', 'ham', 'spam'], dtype=' 0:\n", 192 | " data.append([date, time, author, ' '.join(messageBuffer)])\n", 193 | " messageBuffer.clear()\n", 194 | " date, time, author, message = getDatapoint(line)\n", 195 | " messageBuffer.append(message)\n", 196 | " else:\n", 197 | " messageBuffer.append(line)" 198 | ] 199 | }, 200 | { 201 | "cell_type": "markdown", 202 | "metadata": {}, 203 | "source": [ 204 | "## Now here is how we can analyze the sentiments of WhatsApp chat using Python" 205 | ] 206 | }, 207 | { 208 | "cell_type": "code", 209 | "execution_count": 50, 210 | "metadata": {}, 211 | "outputs": [ 212 | { 213 | "name": "stdout", 214 | "output_type": "stream", 215 | "text": [ 216 | " Date Time Author \\\n", 217 | "2 2022-06-07 11:10 PM Sheikh Rasel Ahmed(Nirob) \n", 218 | "3 2022-06-07 11:11 PM +880 1912-236428 \n", 219 | "4 2022-06-07 11:13 PM Sheikh Rasel Ahmed(Nirob) \n", 220 | "5 2022-06-07 11:15 PM +880 1912-236428 \n", 221 | "6 2022-06-07 11:17 PM Sheikh Rasel Ahmed(Nirob) \n", 222 | "\n", 223 | " Message Positive Negative Neutral \n", 224 | "2 Asscalamu Walaikum 0.0 0.0 1.0 \n", 225 | "3 Walicom as salam 0.0 0.0 1.0 \n", 226 | "4 ki obostha sobar kaj kmn kortecen 0.0 0.0 1.0 \n", 227 | "5 Now in class with Hafiz Bhai. 0.0 0.0 1.0 \n", 228 | "6 ki class 0.0 0.0 1.0 \n" 229 | ] 230 | }, 231 | { 232 | "name": "stderr", 233 | "output_type": "stream", 234 | "text": [ 235 | "C:\\Users\\User\\AppData\\Local\\Temp\\ipykernel_5612\\1075751284.py:7: SettingWithCopyWarning: \n", 236 | "A value is trying to be set on a copy of a slice from a DataFrame.\n", 237 | "Try using .loc[row_indexer,col_indexer] = value instead\n", 238 | "\n", 239 | "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", 240 | " data[\"Positive\"] = [sentiments.polarity_scores(\n", 241 | "C:\\Users\\User\\AppData\\Local\\Temp\\ipykernel_5612\\1075751284.py:9: SettingWithCopyWarning: \n", 242 | "A value is trying to be set on a copy of a slice from a DataFrame.\n", 243 | "Try using .loc[row_indexer,col_indexer] = value instead\n", 244 | "\n", 245 | "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", 246 | " data[\"Negative\"] = [sentiments.polarity_scores(\n", 247 | "C:\\Users\\User\\AppData\\Local\\Temp\\ipykernel_5612\\1075751284.py:11: SettingWithCopyWarning: \n", 248 | "A value is trying to be set on a copy of a slice from a DataFrame.\n", 249 | "Try using .loc[row_indexer,col_indexer] = value instead\n", 250 | "\n", 251 | "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", 252 | " data[\"Neutral\"] = [sentiments.polarity_scores(\n" 253 | ] 254 | } 255 | ], 256 | "source": [ 257 | "from nltk.sentiment.vader import SentimentIntensityAnalyzer\n", 258 | "df = pd.DataFrame(data, columns=[\"Date\", 'Time', 'Author', 'Message'])\n", 259 | "df['Date'] = pd.to_datetime(df['Date'])\n", 260 | "\n", 261 | "data = df.dropna()\n", 262 | "sentiments = SentimentIntensityAnalyzer()\n", 263 | "data[\"Positive\"] = [sentiments.polarity_scores(\n", 264 | " i)[\"pos\"] for i in data[\"Message\"]]\n", 265 | "data[\"Negative\"] = [sentiments.polarity_scores(\n", 266 | " i)[\"neg\"] for i in data[\"Message\"]]\n", 267 | "data[\"Neutral\"] = [sentiments.polarity_scores(\n", 268 | " i)[\"neu\"] for i in data[\"Message\"]]\n", 269 | "print(data.head())" 270 | ] 271 | }, 272 | { 273 | "cell_type": "code", 274 | "execution_count": 51, 275 | "metadata": {}, 276 | "outputs": [ 277 | { 278 | "data": { 279 | "text/html": [ 280 | "
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DateTimeAuthorMessagePositiveNegativeNeutral
22022-06-0711:10 PMSheikh Rasel Ahmed(Nirob)Asscalamu Walaikum0.00.01.0
32022-06-0711:11 PM+880 1912-236428Walicom as salam0.00.01.0
42022-06-0711:13 PMSheikh Rasel Ahmed(Nirob)ki obostha sobar kaj kmn kortecen0.00.01.0
52022-06-0711:15 PM+880 1912-236428Now in class with Hafiz Bhai.0.00.01.0
62022-06-0711:17 PMSheikh Rasel Ahmed(Nirob)ki class0.00.01.0
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PositiveNegativeNeutral
count289.000000289.000000289.000000
mean0.0975540.0228100.865789
std0.2294680.1076070.267685
min0.0000000.0000000.000000
25%0.0000000.0000000.838000
50%0.0000000.0000001.000000
75%0.0000000.0000001.000000
max1.0000001.0000001.000000
\n", 528 | "
" 529 | ], 530 | "text/plain": [ 531 | " Positive Negative Neutral\n", 532 | "count 289.000000 289.000000 289.000000\n", 533 | "mean 0.097554 0.022810 0.865789\n", 534 | "std 0.229468 0.107607 0.267685\n", 535 | "min 0.000000 0.000000 0.000000\n", 536 | "25% 0.000000 0.000000 0.838000\n", 537 | "50% 0.000000 0.000000 1.000000\n", 538 | "75% 0.000000 0.000000 1.000000\n", 539 | "max 1.000000 1.000000 1.000000" 540 | ] 541 | }, 542 | "execution_count": 53, 543 | "metadata": {}, 544 | "output_type": "execute_result" 545 | } 546 | ], 547 | "source": [ 548 | "data.describe()" 549 | ] 550 | }, 551 | { 552 | "cell_type": "code", 553 | "execution_count": 54, 554 | "metadata": {}, 555 | "outputs": [ 556 | { 557 | "name": "stdout", 558 | "output_type": "stream", 559 | "text": [ 560 | "Neutral 🙂 \n" 561 | ] 562 | } 563 | ], 564 | "source": [ 565 | "x = sum(data[\"Positive\"])\n", 566 | "y = sum(data[\"Negative\"])\n", 567 | "z = sum(data[\"Neutral\"])\n", 568 | "\n", 569 | "\n", 570 | "def sentiment_score(a, b, c):\n", 571 | " if (a > b) and (a > c):\n", 572 | " print(\"Positive 😊 \")\n", 573 | " elif (b > a) and (b > c):\n", 574 | " print(\"Negative 😠 \")\n", 575 | " else:\n", 576 | " print(\"Neutral 🙂 \")\n", 577 | "\n", 578 | "\n", 579 | "sentiment_score(x, y, z)" 580 | ] 581 | }, 582 | { 583 | "cell_type": "markdown", 584 | "metadata": {}, 585 | "source": [ 586 | "So, the data I used indicates that most of the messages between me and the other person are neutral. Which means it’s neither positive nor negative." 587 | ] 588 | }, 589 | { 590 | "cell_type": "markdown", 591 | "metadata": {}, 592 | "source": [ 593 | "## Summary" 594 | ] 595 | }, 596 | { 597 | "cell_type": "markdown", 598 | "metadata": {}, 599 | "source": [ 600 | "So this is how we can perform the task of sentiment analysis of WhatsApp chat. WhatsApp is a great source of data for the task of sentiment analysis and every data science task based on natural language processing. I hope you liked this article on the task of WhatsApp chat sentiment analysis using Python. Feel free to ask your valuable questions in the comments section below." 601 | ] 602 | }, 603 | { 604 | "cell_type": "markdown", 605 | "metadata": {}, 606 | "source": [ 607 | "## Sheikh Rasel Ahmed" 608 | ] 609 | }, 610 | { 611 | "cell_type": "markdown", 612 | "metadata": {}, 613 | "source": [ 614 | "Data Science || Machine Learning || Deep Learning || Artificial Intelligence Enthusiast" 615 | ] 616 | }, 617 | { 618 | "cell_type": "code", 619 | "execution_count": 55, 620 | "metadata": {}, 621 | "outputs": [], 622 | "source": [ 623 | "# LinkedIn - https://www.linkedin.com/in/shekhnirob1\n", 624 | "\n", 625 | "# GitHub - https://github.com/Rasel1435\n", 626 | "\n", 627 | "# Behance - https://www.behance.net/Shekhrasel2513\n" 628 | ] 629 | } 630 | ], 631 | "metadata": { 632 | "kernelspec": { 633 | "display_name": "Python 3.10.5 64-bit", 634 | "language": "python", 635 | "name": "python3" 636 | }, 637 | "language_info": { 638 | "codemirror_mode": { 639 | "name": "ipython", 640 | "version": 3 641 | }, 642 | "file_extension": ".py", 643 | "mimetype": "text/x-python", 644 | "name": "python", 645 | "nbconvert_exporter": "python", 646 | "pygments_lexer": "ipython3", 647 | "version": "3.10.5" 648 | }, 649 | "orig_nbformat": 4, 650 | "vscode": { 651 | "interpreter": { 652 | "hash": "afb734500600fd355917ca529030176ea0ca205570884b88f2f6f7d791fd3fbe" 653 | } 654 | } 655 | }, 656 | "nbformat": 4, 657 | "nbformat_minor": 2 658 | } 659 | -------------------------------------------------------------------------------- /student_project/Dataset.xlsx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Rasel1435/Machine-Learning-Projects-with-Python-for-Beginners/60231f07b76f0fe200226cc383857c22d2fed571/student_project/Dataset.xlsx --------------------------------------------------------------------------------