diff --git a/Students/ksenia/D&J-IND_101001_171001.txt b/Students/ksenia/D&J-IND_101001_171001.txt
new file mode 100644
index 0000000..3f67b3a
--- /dev/null
+++ b/Students/ksenia/D&J-IND_101001_171001.txt
@@ -0,0 +1,2135 @@
+<TICKER>,<PER>,<DATE>,<TIME>,<CLOSE>,<VOL>
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diff --git a/Students/ksenia/GAZP_101001_171001.txt b/Students/ksenia/GAZP_101001_171001.txt
new file mode 100644
index 0000000..5c5598c
--- /dev/null
+++ b/Students/ksenia/GAZP_101001_171001.txt
@@ -0,0 +1,1759 @@
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+GAZP,D,20170407,000000,128.7200000,27362790
+GAZP,D,20170410,000000,126.1600000,29799810
+GAZP,D,20170411,000000,126.0000000,32626680
+GAZP,D,20170412,000000,125.0500000,28702040
+GAZP,D,20170413,000000,124.5400000,42139090
+GAZP,D,20170414,000000,122.8500000,14689130
+GAZP,D,20170417,000000,123.7000000,21595230
+GAZP,D,20170418,000000,122.2200000,22081750
+GAZP,D,20170419,000000,121.5500000,28911230
+GAZP,D,20170420,000000,121.9900000,32955940
+GAZP,D,20170421,000000,123.0000000,29465300
+GAZP,D,20170424,000000,128.0000000,66751830
+GAZP,D,20170425,000000,130.2000000,57382670
+GAZP,D,20170426,000000,133.4300000,57635150
+GAZP,D,20170427,000000,133.4100000,99542840
+GAZP,D,20170428,000000,136.7500000,52186090
+GAZP,D,20170502,000000,136.5200000,33809990
+GAZP,D,20170503,000000,135.8100000,33129650
+GAZP,D,20170504,000000,134.9300000,31945660
+GAZP,D,20170505,000000,134.4100000,26216280
+GAZP,D,20170510,000000,134.1900000,23714000
+GAZP,D,20170511,000000,131.1000000,36600410
+GAZP,D,20170512,000000,132.5000000,23886540
+GAZP,D,20170515,000000,131.5000000,46356520
+GAZP,D,20170516,000000,131.1300000,22519330
+GAZP,D,20170517,000000,132.0700000,17611290
+GAZP,D,20170518,000000,126.9000000,80113110
+GAZP,D,20170519,000000,124.1900000,57664570
+GAZP,D,20170522,000000,122.5200000,39193160
+GAZP,D,20170523,000000,123.2300000,34369260
+GAZP,D,20170524,000000,122.0000000,32248550
+GAZP,D,20170525,000000,121.6100000,24546840
+GAZP,D,20170526,000000,122.2000000,22646890
+GAZP,D,20170529,000000,123.1100000,24470000
+GAZP,D,20170530,000000,122.1000000,16057320
+GAZP,D,20170531,000000,120.2800000,55389010
+GAZP,D,20170601,000000,118.0000000,48558490
+GAZP,D,20170602,000000,119.6900000,38260450
+GAZP,D,20170605,000000,119.0000000,24767790
+GAZP,D,20170606,000000,119.0500000,18193840
+GAZP,D,20170607,000000,118.9200000,27870030
+GAZP,D,20170608,000000,119.3000000,27514310
+GAZP,D,20170609,000000,120.5000000,21294170
+GAZP,D,20170613,000000,118.1500000,31058980
+GAZP,D,20170614,000000,115.3500000,47003760
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+GAZP,D,20170616,000000,116.0200000,40336010
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+GAZP,D,20170620,000000,117.4400000,29930700
+GAZP,D,20170621,000000,117.9700000,23560060
+GAZP,D,20170622,000000,119.5500000,31513190
+GAZP,D,20170623,000000,119.1000000,23303360
+GAZP,D,20170626,000000,118.6700000,18274640
+GAZP,D,20170627,000000,119.3500000,18365790
+GAZP,D,20170628,000000,119.7000000,21106940
+GAZP,D,20170629,000000,119.1600000,26435690
+GAZP,D,20170630,000000,118.4900000,22432000
+GAZP,D,20170703,000000,119.8600000,18087840
+GAZP,D,20170704,000000,122.7300000,37693640
+GAZP,D,20170705,000000,122.7000000,26052360
+GAZP,D,20170706,000000,122.6800000,25151140
+GAZP,D,20170707,000000,123.0000000,24725120
+GAZP,D,20170710,000000,124.2500000,27166510
+GAZP,D,20170711,000000,124.7800000,20642420
+GAZP,D,20170712,000000,124.8700000,22701550
+GAZP,D,20170713,000000,125.5500000,29387490
+GAZP,D,20170714,000000,125.0000000,21555180
+GAZP,D,20170717,000000,125.1400000,23810200
+GAZP,D,20170718,000000,124.4900000,39544710
+GAZP,D,20170719,000000,120.1100000,42498820
+GAZP,D,20170720,000000,120.6000000,21786790
+GAZP,D,20170721,000000,118.9500000,20841430
+GAZP,D,20170724,000000,119.1700000,22726670
+GAZP,D,20170725,000000,118.4000000,23673000
+GAZP,D,20170726,000000,119.0000000,22310500
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+GAZP,D,20170731,000000,116.1000000,40277050
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diff --git a/Students/ksenia/LinRegAlgorithm.py b/Students/ksenia/LinRegAlgorithm.py
new file mode 100644
index 0000000..7498c86
--- /dev/null
+++ b/Students/ksenia/LinRegAlgorithm.py
@@ -0,0 +1,196 @@
+
+# coding: utf-8
+
+# Author: K. Khmelevskaya
+
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+
+
+# Загрузка данных
+dj = pd.read_csv('D&J-IND_101001_171001.txt')
+gasp = pd.read_csv('GAZP_101001_171001.txt')
+yndx = pd.read_csv('YNDX_101001_171001.txt')
+print(dj.shape, gasp.shape, yndx.shape)
+
+
+# Изначальная модель с 2мя параметрами
+x1 = gasp['<CLOSE>']
+x2 = gasp['<CLOSE>']
+y = dj['<CLOSE>']
+
+# Предварительная обработка данных
+res1 = pd.merge(dj, gasp, on='<DATE>', suffixes=['_DJ', '_GAZP'])
+res2 = pd.merge(res1, yndx, on='<DATE>', suffixes=['', '_YNDX'])
+x1 = res2['<CLOSE>_DJ']
+x2 = res2['<CLOSE>']
+y = res2['<CLOSE>_GAZP']
+
+# Нормализация данных
+x1min, x1max, x2min, x2max, ymin, ymax = min(x1), max(x1), min(x2), max(x2), min(y), max(y)
+x1 = (x1 - min(x1)) / (max(x1) - min(x1))
+x2 = (x2 - min(x2)) / (max(x2) - min(x2))
+y = (y - min(y)) / (max(y) - min(y))
+
+datasets = [x1, x2]
+
+# Класс, отвечающий за обучение парной линейной регрессии
+class hypothesis(object):
+    def __init__(self):
+        self.theta = np.array([0, 0, 0])
+    # Метод, возвращающий теоретический результат по переданным значениям факторов
+    def apply(self, X1, X2):
+        return self.theta[0] + self.theta[1] * X1 + self.theta[2] * X2
+    # Функция ошибки
+    def error(self, X1, X2, Y):
+        return sum((self.apply(X1, X2) - Y)**2) / (2 * len(Y))
+    # Метод, реализующий градиентный спуск
+    def gradient_descent(self, x1, x2, y):
+        i = 0
+        m = len(x1)
+        steps = []
+        errors = []
+        while(i < 100):
+            y_ = hyp.apply(x1, x2)
+            dJ0 = sum(y_ - y) / m
+            dJ1 = sum((y_ - y)*x1) / m
+            dJ2 = sum((y_ - y)*x2)/ m
+            
+            alpha = 0.9
+            theta0 = self.theta[0] - alpha * dJ0
+            theta1 = self.theta[1] - alpha * dJ1
+            theta2 = self.theta[2] - alpha * dJ2
+            self.theta = np.array([theta0, theta1, theta2])
+            
+            J = hyp.error(x1, x2, y)
+            
+            steps.append(i)
+            errors.append(J)
+            
+            i += 1
+        
+        return (steps, errors)
+
+# Заводим и обучаем нашу регрессию
+hyp = hypothesis()
+
+y_ = hyp.apply(x1, x2)
+
+J = hyp.error(x1, x2, y)
+print(J)
+
+(steps, errors) = hyp.gradient_descent(x1, x2, y)
+
+y_ = hyp.apply(x1, x2)
+
+plt.plot(steps, errors)
+plt.show()
+
+# Денормализуем данные и параметры регрессии
+x1 = x1 * (x1max - x1min) + x1min
+x2 = x2 * (x2max - x2min) + x2min
+y = y * (ymax - ymin) + ymin
+
+theta1 = hyp.theta[1] * (ymax - ymin) / (x1max - x1min)
+theta2 = hyp.theta[2] * (ymax - ymin) / (x2max - x2min)
+theta0 = hyp.theta[0] * (ymax - ymin) + ymin - theta1*x1min - theta2*x1min
+hyp.theta = np.array([theta0, theta1, theta2])
+J = hyp.error(x1, x2, y)
+print(J)
+
+
+# Универсальная модель для любого количествтва параметров
+# Для сравнения работы с предыдущим вариантом используются те же массивы данных
+x1 = gasp['<CLOSE>']
+x2 = gasp['<CLOSE>']
+y = dj['<CLOSE>']
+
+# Предварительная обработка данных
+res1 = pd.merge(dj, gasp, on='<DATE>', suffixes=['_DJ', '_GAZP'])
+res2 = pd.merge(res1, yndx, on='<DATE>', suffixes=['', '_YNDX'])
+x1 = res2['<CLOSE>_DJ']
+x2 = res2['<CLOSE>']
+y = res2['<CLOSE>_GAZP']
+
+# Нормализация данных
+datasets = [x1, x2]
+datasets = map(lambda x: (x - min(x)) / (max(x) - min(x)), datasets)
+y = (y - min(y)) / (max(y) - min(y))
+
+class hypothesis2(object):
+    def __init__(self):
+        self.theta = np.array([0]*(len(datasets) + 1))
+    # Метод, возвращающий теоретический результат по переданным значениям факторов
+    def apply(self, dataarr):
+    # dataarr - массив из наборов данных
+        func = self.theta[0]
+        i = 1
+        for el in dataarr:
+            if i <= len(dataarr):
+                func += self.theta[i] * el
+            i += 1
+        return func
+        
+    # Функция ошибки
+    def error(self, dataarr, Y):
+        return sum((self.apply(dataarr) - Y)**2) / (2 * len(Y))
+    # Метод, реализующий градиентный спуск
+    def gradient_descent(self, dataarr, y):
+        i = 0
+        m = len(dataarr[0])
+        steps = []
+        errors = []
+        while(i < 100):
+            y_ = hyp.apply(dataarr)
+            dJ0 = sum(y_ - y) / m
+            dJn = [dJ0]
+            for el in dataarr:
+                dJn.append(sum((y_ - y) * el)/m)
+            alpha = 0.9
+            pairs = zip(self.theta, dJn)
+            pairs = map(lambda x: list(x), pairs)
+            for pair in pairs:
+                pair[0] -= alpha * pair[1]
+            self.theta = np.array(map(lambda x: x[0], pairs))
+            
+            J = hyp.error(dataarr, y)
+            steps.append(i)
+            errors.append(J)
+            
+            i += 1
+        
+        return (steps, errors)
+
+# Заводим и обучаем нашу регрессию
+hyp = hypothesis2()
+
+y_ = hyp.apply(datasets)
+
+J = hyp.error(datasets, y)
+print(J)
+
+(steps, errors) = hyp.gradient_descent(datasets, y)
+
+y_ = hyp.apply(datasets)
+
+plt.plot(steps, errors)
+plt.show()
+
+# Денормализуем данные и параметры регрессии
+for dataset in datasets:
+    dataset = dataset * (max(dataset) - min(dataset)) + min(dataset)
+
+y = y * (ymax - ymin) + ymin
+
+pairs = zip(hyp.theta[1:], datasets)
+pairs = map(lambda x: list(x), pairs)
+coefs = 0
+for el in pairs:
+    el[0] = el[0] * (ymax - ymin) / (max(el[1]) - min(el[1]))
+    coefs -= el[0]*min(el[1])
+hyp.theta[1:] = np.array(map(lambda x: x[0], pairs))
+hyp.theta[0] = hyp.theta[0] * (ymax - ymin) + coefs
+
+J = hyp.error(datasets, y)
+print(J)
diff --git a/Students/ksenia/YNDX_101001_171001.txt b/Students/ksenia/YNDX_101001_171001.txt
new file mode 100644
index 0000000..4b29090
--- /dev/null
+++ b/Students/ksenia/YNDX_101001_171001.txt
@@ -0,0 +1,839 @@
+<TICKER>,<PER>,<DATE>,<TIME>,<CLOSE>,<VOL>
+YNDX,D,20140604,000000,1170.0000000,35146
+YNDX,D,20140605,000000,1145.1000000,24973
+YNDX,D,20140606,000000,1163.0000000,7889
+YNDX,D,20140609,000000,1172.1000000,7304
+YNDX,D,20140610,000000,1170.0000000,3207
+YNDX,D,20140611,000000,1179.9000000,8837
+YNDX,D,20140616,000000,1165.0000000,13904
+YNDX,D,20140617,000000,1185.5000000,8517
+YNDX,D,20140618,000000,1182.9000000,1455
+YNDX,D,20140619,000000,1168.0000000,1396
+YNDX,D,20140620,000000,1175.0000000,2702
+YNDX,D,20140623,000000,1196.0000000,14081
+YNDX,D,20140624,000000,1191.8000000,3895
+YNDX,D,20140625,000000,1176.8000000,3949
+YNDX,D,20140626,000000,1185.0000000,2392
+YNDX,D,20140627,000000,1185.0000000,1148
+YNDX,D,20140630,000000,1218.0000000,8749
+YNDX,D,20140701,000000,1218.2000000,3036
+YNDX,D,20140702,000000,1200.0000000,33473
+YNDX,D,20140703,000000,1192.0000000,25562
+YNDX,D,20140704,000000,1186.0000000,2133
+YNDX,D,20140707,000000,1160.0000000,19359
+YNDX,D,20140708,000000,1113.4000000,26565
+YNDX,D,20140709,000000,1094.0000000,19416
+YNDX,D,20140710,000000,1077.8000000,11216
+YNDX,D,20140711,000000,1099.3000000,35563
+YNDX,D,20140714,000000,1118.9000000,6192
+YNDX,D,20140715,000000,1114.3000000,2789
+YNDX,D,20140716,000000,1106.5000000,7340
+YNDX,D,20140717,000000,1088.0000000,7221
+YNDX,D,20140718,000000,1072.3000000,7144
+YNDX,D,20140721,000000,1047.5000000,8353
+YNDX,D,20140722,000000,1060.0000000,10468
+YNDX,D,20140723,000000,1056.2000000,5286
+YNDX,D,20140724,000000,1077.8000000,5186
+YNDX,D,20140725,000000,1075.1000000,5127
+YNDX,D,20140728,000000,1055.0000000,3992
+YNDX,D,20140729,000000,1116.0000000,11794
+YNDX,D,20140730,000000,1120.0000000,3075
+YNDX,D,20140731,000000,1087.7000000,4886
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