{"id":3572,"date":"2023-07-16T18:41:15","date_gmt":"2023-07-16T18:41:15","guid":{"rendered":"https:\/\/statorials.org\/tr\/istatistik-modelleri-tahmin-ediyor\/"},"modified":"2023-07-16T18:41:15","modified_gmt":"2023-07-16T18:41:15","slug":"istatistik-modelleri-tahmin-ediyor","status":"publish","type":"post","link":"https:\/\/statorials.org\/tr\/istatistik-modelleri-tahmin-ediyor\/","title":{"rendered":"Statsmodels'de regresyon modeli kullan\u0131larak tahminler nas\u0131l yap\u0131l\u0131r?"},"content":{"rendered":"

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Yeni g\u00f6zlemler hakk\u0131nda tahminlerde bulunmak amac\u0131yla Python’daki statsmodels<\/a> mod\u00fcl\u00fcn\u00fc kullanarak regresyon modeli uydurmay\u0131 kullanmak i\u00e7in a\u015fa\u011f\u0131daki temel s\u00f6zdizimini kullanabilirsiniz:<\/span><\/p>\n

 model. predict<\/span> (df_new)\n<\/strong><\/pre>\n
 Bu \u00f6zel s\u00f6zdizimi, model<\/strong> ad\u0131 verilen istatistiksel modellere uygun bir regresyon modeli kullanarak df_new<\/strong> ad\u0131 verilen yeni bir DataFrame’in her sat\u0131r\u0131 i\u00e7in \u00f6ng\u00f6r\u00fclen yan\u0131t de\u011ferlerini hesaplayacakt\u0131r.<\/span><\/p>\n
 A\u015fa\u011f\u0131daki \u00f6rnek, bu s\u00f6zdiziminin pratikte nas\u0131l kullan\u0131laca\u011f\u0131n\u0131 g\u00f6sterir.<\/span><\/p>\n
 \u00d6rnek: Statsmodels’de bir regresyon modeli kullanarak tahminlerde bulunmak<\/strong><\/span><\/h2>\n
 Belirli bir s\u0131n\u0131ftaki \u00f6\u011frencilerin \u00e7al\u0131\u015f\u0131lan saatler, girilen haz\u0131rl\u0131k s\u0131navlar\u0131 ve ald\u0131klar\u0131 final notlar\u0131 hakk\u0131nda bilgi i\u00e7eren a\u015fa\u011f\u0131daki panda DataFrame’e sahip oldu\u011fumuzu varsayal\u0131m:<\/span><\/p>\n
 import<\/span> pandas as<\/span> pd\n\n#createDataFrame\n<\/span>df = pd. DataFrame<\/span> ({' hours<\/span> ': [1, 2, 2, 4, 2, 1, 5, 4, 2, 4, 4, 3, 6],\n                   ' exams<\/span> ': [1, 3, 3, 5, 2, 2, 1, 1, 0, 3, 4, 3, 2],\n                   ' score<\/span> ': [76, 78, 85, 88, 72, 69, 94, 94, 88, 92, 90, 75, 96]})\n\n#view head of DataFrame\n<\/span>df. head<\/span> ()\n\n\thours exam score\n0 1 1 76\n1 2 3 78\n2 2 3 85\n3 4 5 88\n4 2 2 72<\/strong><\/pre>\n
 import<\/span> statsmodels. api<\/span> as<\/span> sm\n\n#define predictor and response variables\n<\/span>y = df[' score<\/span> ']\nx = df[[' hours<\/span> ', ' exams<\/span> ']]\n\n#add constant to predictor variables\n<\/span>x = sm. add_constant<\/span> (x)\n\n#fit linear regression model\n<\/span>model = sm. OLS<\/span> (y,x). fit<\/span> ()\n\n#view model summary\n<\/span>print<\/span> ( model.summary<\/span> ())\n\n                            OLS Regression Results                            \n==================================================== ============================\nDept. Variable: R-squared score: 0.718\nModel: OLS Adj. R-squared: 0.661\nMethod: Least Squares F-statistic: 12.70\nDate: Fri, 05 Aug 2022 Prob (F-statistic): 0.00180\nTime: 09:24:38 Log-Likelihood: -38.618\nNo. Observations: 13 AIC: 83.24\nDf Residuals: 10 BIC: 84.93\nDf Model: 2                                         \nCovariance Type: non-robust                                         \n==================================================== ============================\n                 coef std err t P>|t| [0.025 0.975]\n-------------------------------------------------- ----------------------------\nconst 71.4048 4.001 17.847 0.000 62.490 80.319\nhours 5.1275 1.018 5.038 0.001 2.860 7.395\nexams -1.2121 1.147 -1.057 0.315 -3.768 1.344\n==================================================== ============================\nOmnibus: 1,103 Durbin-Watson: 1,248\nProb(Omnibus): 0.576 Jarque-Bera (JB): 0.803\nSkew: -0.289 Prob(JB): 0.669\nKurtosis: 1.928 Cond. No. 11.7\n==================================================== ============================\n<\/strong><\/pre>\n
 \u00c7\u0131kt\u0131daki katsay\u0131<\/strong> s\u00fctunundan uygun regresyon modelini yazabiliriz:<\/span><\/p>\n
 Puan = 71.4048 + 5.1275 (saat) \u2013 1.2121 (s\u0131navlar)<\/span><\/p>\n
 \u015eimdi be\u015f yeni \u00f6\u011frencinin \u201cpuan\u0131n\u0131\u201d tahmin etmek i\u00e7in uygun regresyon modelini kullanmak istedi\u011fimizi varsayal\u0131m.<\/span><\/p>\n
 \u00d6ncelikle be\u015f yeni g\u00f6zlemi tutacak bir DataFrame olu\u015ftural\u0131m:<\/span><\/p>\n
 #create new DataFrame\ndf_new = pd. DataFrame<\/span> ({' hours<\/span> ': [1, 2, 2, 4, 5],\n                       ' exams<\/span> ': [1, 1, 4, 3, 3]})<\/span>\n\n#add column for constant\ndf_new = sm. add_constant<\/span> (df_new)\n<\/span>\n#view new DataFrame\nprint<\/span> (df_new)\n\n   const hours exams\n0 1.0 1 1\n1 1.0 2 1\n2 1.0 2 4\n3 1.0 4 3\n4 1.0 5 3<\/span><\/span><\/strong><\/pre>\n
 Daha sonra, uygun regresyon modelimizde \u00f6ng\u00f6r\u00fcc\u00fc de\u011fi\u015fkenlerin de\u011ferleri olarak “saat” ve “s\u0131navlar\u0131” kullanarak, bu \u00f6\u011frencilerin her birinin “puan\u0131n\u0131” tahmin etmek i\u00e7in tahmin()<\/strong> i\u015flevini kullanabiliriz:<\/span><\/span><\/p>\n
 #predict scores for the five new students<\/span>\nmodel. predict<\/span> (df_new)\n\n0 75.320242\n1 80.447734\n2 76.811480\n3 88.278550\n4 93.406042\ndtype:float64\n<\/strong><\/pre>\n
 Sonucun nas\u0131l yorumlanaca\u011f\u0131 a\u015fa\u011f\u0131da a\u00e7\u0131klanm\u0131\u015ft\u0131r:<\/span><\/p>\n
 Tahmin edici de\u011fi\u015fkenler olarak “saatler” ve “s\u0131navlar”\u0131 ve yan\u0131t de\u011fi\u015fkeni olarak “puan”\u0131 kullanarak \u00e7oklu do\u011frusal regresyon modeline<\/a> uyum sa\u011flamak i\u00e7in statsmodels mod\u00fcl\u00fcn\u00fcn OLS()<\/strong> i\u015flevini kullanabiliriz:<\/span><\/p>\n