diff --git a/week1/Exercise2.ipynb b/week1/Exercise2.ipynb
index 6cd775b..ff2dff9 100644
--- a/week1/Exercise2.ipynb
+++ b/week1/Exercise2.ipynb
@@ -50,7 +50,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -85,35 +85,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "    population  householdsize  racepctblack  racePctWhite  racePctAsian  \\\n",
-      "9       103590           2.62         23.14         67.60          0.92   \n",
-      "13       57140           2.74         53.52         45.65          0.49   \n",
-      "17      180038           2.62          1.30         74.02         14.14   \n",
-      "19      261721           2.60          8.41         82.64          3.92   \n",
-      "21     7322564           2.60         28.71         52.26          7.00   \n",
-      "\n",
-      "    racePctHisp  agePct12t21  agePct12t29  agePct16t24  agePct65up  \n",
-      "9         16.35        19.88        34.55        21.62       13.12  \n",
-      "13         0.43        16.51        28.17        14.68       13.38  \n",
-      "17        20.96        12.04        26.68        12.37       11.54  \n",
-      "19         8.91        14.18        32.78        15.14        4.58  \n",
-      "21        24.36        13.06        27.46        13.09       11.62  \n",
-      "9     26.88\n",
-      "13    27.26\n",
-      "17     5.02\n",
-      "19     2.39\n",
-      "21    26.59\n",
-      "Name: murdPerPop, dtype: float64\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "def printDataSet():\n",
     "    (X_crime,y_crime) = get_crime_dataset()\n",
@@ -126,20 +100,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(0.59690236423467447, 0.67949819476183415)"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "#perform linear regression\n",
     "def ex1():\n",
@@ -161,20 +124,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(0.78061368287329047, -0.055156513114217542)"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "#perform polynomial regression of degree 2\n",
     "def ex1a():\n",
@@ -196,20 +148,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(0.7577837996817649, 0.42712193149261213)"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# to avoid the problems of overfitting polynomial regression of higher degrees, we put a penalty on the\n",
     "# coefficients that are large.\n",
@@ -236,20 +177,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(0.56772731184584013, 0.65054477060117732)"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# when features vary wildly, e.g when calculating the price of the house: the square footage is in the thousands\n",
     "# and number of bedrooms is in the single digits, it's best to normalize the data to values \n",
@@ -280,30 +210,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Alpha = 0.00\n",
-      "r-squared training: 1.00, r-squared test: -1485.23\n",
-      "Alpha = 1.00\n",
-      "r-squared training: 0.68, r-squared test: 0.65\n",
-      "Alpha = 10.00\n",
-      "r-squared training: 0.62, r-squared test: 0.67\n",
-      "Alpha = 20.00\n",
-      "r-squared training: 0.60, r-squared test: 0.67\n",
-      "Alpha = 50.00\n",
-      "r-squared training: 0.58, r-squared test: 0.65\n",
-      "Alpha = 100.00\n",
-      "r-squared training: 0.55, r-squared test: 0.63\n",
-      "Alpha = 1000.00\n",
-      "r-squared training: 0.27, r-squared test: 0.30\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# use min max scaler and ridge regressor with alpha values in [0, 1, 10, 20, 50, 100, 1000] with a polynomial\n",
     "# of degree 3\n",
@@ -340,34 +249,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Alpha = 0.10\n",
-      "r-squared training: 0.63, r-squared test: 0.69\n",
-      "Alpha = 0.50\n",
-      "r-squared training: 0.53, r-squared test: 0.62\n",
-      "Alpha = 1.00\n",
-      "r-squared training: 0.43, r-squared test: 0.52\n",
-      "Alpha = 2.00\n",
-      "r-squared training: 0.16, r-squared test: 0.19\n",
-      "Alpha = 3.00\n",
-      "r-squared training: 0.00, r-squared test: -0.00\n",
-      "Alpha = 5.00\n",
-      "r-squared training: 0.00, r-squared test: -0.00\n",
-      "Alpha = 10.00\n",
-      "r-squared training: 0.00, r-squared test: -0.00\n",
-      "Alpha = 20.00\n",
-      "r-squared training: 0.00, r-squared test: -0.00\n",
-      "Alpha = 50.00\n",
-      "r-squared training: 0.00, r-squared test: -0.00\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Lasso Regression\n",
     "# another way of doing regularization is using the Lasso Regression, which also penalizes the coeficients\n",
@@ -406,17 +290,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.48715114223 0.562719985433\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Support Vector Machines transform of the data before finding a match \n",
     "def ex5():\n",
@@ -443,18 +319,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'C': 100.0}\n",
-      "0.561732591767 0.639787265159\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Use GridSearchCV to run SVR with different parameters \n",
     "def ex6():\n",
diff --git a/week1/ex1data1.txt b/week1/ex1data1.txt
deleted file mode 100644
index 1a32414..0000000
--- a/week1/ex1data1.txt
+++ /dev/null
@@ -1,98 +0,0 @@
-population,sales
-6.1101,17.592
-5.5277,9.1302
-8.5186,13.662
-7.0032,11.854
-5.8598,6.8233
-8.3829,11.886
-7.4764,4.3483
-8.5781,12
-6.4862,6.5987
-5.0546,3.8166
-5.7107,3.2522
-14.164,15.505
-5.734,3.1551
-8.4084,7.2258
-5.6407,0.71618
-5.3794,3.5129
-6.3654,5.3048
-5.1301,0.56077
-6.4296,3.6518
-7.0708,5.3893
-6.1891,3.1386
-20.27,21.767
-5.4901,4.263
-6.3261,5.1875
-5.5649,3.0825
-18.945,22.638
-12.828,13.501
-10.957,7.0467
-13.176,14.692
-22.203,24.147
-5.2524,-1.22
-6.5894,5.9966
-9.2482,12.134
-5.8918,1.8495
-8.2111,6.5426
-7.9334,4.5623
-8.0959,4.1164
-5.6063,3.3928
-12.836,10.117
-6.3534,5.4974
-5.4069,0.55657
-6.8825,3.9115
-11.708,5.3854
-5.7737,2.4406
-7.8247,6.7318
-7.0931,1.0463
-5.0702,5.1337
-5.8014,1.844
-11.7,8.0043
-5.5416,1.0179
-7.5402,6.7504
-5.3077,1.8396
-7.4239,4.2885
-7.6031,4.9981
-6.3328,1.4233
-6.3589,-1.4211
-6.2742,2.4756
-5.6397,4.6042
-9.3102,3.9624
-9.4536,5.4141
-8.8254,5.1694
-5.1793,-0.74279
-21.279,17.929
-14.908,12.054
-18.959,17.054
-7.2182,4.8852
-8.2951,5.7442
-10.236,7.7754
-5.4994,1.0173
-20.341,20.992
-10.136,6.6799
-7.3345,4.0259
-6.0062,1.2784
-7.2259,3.3411
-5.0269,-2.6807
-6.5479,0.29678
-7.5386,3.8845
-5.0365,5.7014
-10.274,6.7526
-5.1077,2.0576
-5.7292,0.47953
-5.1884,0.20421
-6.3557,0.67861
-9.7687,7.5435
-6.5159,5.3436
-8.5172,4.2415
-9.1802,6.7981
-6.002,0.92695
-5.5204,0.152
-5.0594,2.8214
-5.7077,1.8451
-7.6366,4.2959
-5.8707,7.2029
-5.3054,1.9869
-8.2934,0.14454
-13.394,9.0551
-5.4369,0.61705
diff --git a/week1/ex1data2.txt b/week1/ex1data2.txt
deleted file mode 100644
index 6c5acd9..0000000
--- a/week1/ex1data2.txt
+++ /dev/null
@@ -1,48 +0,0 @@
-population,valy,sales
-2104,3,399900
-1600,3,329900
-2400,3,369000
-1416,2,232000
-3000,4,539900
-1985,4,299900
-1534,3,314900
-1427,3,198999
-1380,3,212000
-1494,3,242500
-1940,4,239999
-2000,3,347000
-1890,3,329999
-4478,5,699900
-1268,3,259900
-2300,4,449900
-1320,2,299900
-1236,3,199900
-2609,4,499998
-3031,4,599000
-1767,3,252900
-1888,2,255000
-1604,3,242900
-1962,4,259900
-3890,3,573900
-1100,3,249900
-1458,3,464500
-2526,3,469000
-2200,3,475000
-2637,3,299900
-1839,2,349900
-1000,1,169900
-2040,4,314900
-3137,3,579900
-1811,4,285900
-1437,3,249900
-1239,3,229900
-2132,4,345000
-4215,4,549000
-2162,4,287000
-1664,2,368500
-2238,3,329900
-2567,4,314000
-1200,3,299000
-852,2,179900
-1852,4,299900
-1203,3,239500
diff --git a/week1/ex3.py b/week1/ex3.py
deleted file mode 100644
index 5df6b16..0000000
--- a/week1/ex3.py
+++ /dev/null
@@ -1,41 +0,0 @@
-import pandas as pd
-import matplotlib.pyplot as plt
-import numpy as np
-from matplotlib.colors import ListedColormap
-from sklearn.neighbors import KNeighborsClassifier
-from sklearn.model_selection import train_test_split
-from sklearn import svm, metrics
-
-df = pd.read_csv('ex2data1.txt',header=None)
-df.columns = ['test1','test2','pass']
-notPassed = df[df['pass'] == 0]
-passed = df[df['pass'] == 1]
-plt.plot(notPassed['test1'],notPassed['test2'],'yo')
-plt.plot(passed['test1'],passed['test2'],'bo')
-plt.show()
-
-X = df[['test1','test2']]
-y = df['pass']
-
-# h = .01
-h = 1
-cmap_light = ListedColormap(['#FFAAAA', '#AAFFAA', '#AAAAFF'])
-X_train, X_test, y_train, y_test = train_test_split(X,y, random_state=0)
-neigh = KNeighborsClassifier(n_neighbors=3).fit(X_train,y_train)
-print (neigh.score(X_train, y_train),neigh.score(X_test, y_test))
-# x_min, x_max = df['test1'].min() - 3, df['test1'].max() + 3
-# y_min, y_max = df['test2'].min() - 3, df['test2'].max() + 3
-x_min, x_max = df['test1'].min() - .2, df['test1'].max() + .2
-y_min, y_max = df['test2'].min() - .2, df['test2'].max() + .2
-xx, yy = np.meshgrid(np.arange(x_min, x_max, h),
-                        np.arange(y_min, y_max, h))
-Z = neigh.predict(np.c_[xx.ravel(), yy.ravel()])
-# PREDICTED = neigh.predict(X_test)
-# metrics.classification_report(y_test,PREDICTED)
-
-# Put the result into a color plot
-Z = Z.reshape(xx.shape)
-plt.pcolormesh(xx, yy, Z, cmap=cmap_light)
-plt.plot(notPassed['test1'],notPassed['test2'],'yo')
-plt.plot(passed['test1'],passed['test2'],'bo')
-plt.show()
\ No newline at end of file
diff --git a/week1/exercise1.ipynb b/week1/exercise1.ipynb
index 2498261..777a43c 100644
--- a/week1/exercise1.ipynb
+++ b/week1/exercise1.ipynb
@@ -25,36 +25,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# let's generate some random data that we want to find an equation for\n",
+    "# let's read price of stock over time\n",
     "def getXAndY():\n",
-    "    import numpy as np\n",
-    "    np.random.seed(0)\n",
-    "    n = 15\n",
-    "    x = np.linspace(0,10,n) + np.random.randn(n)/5\n",
-    "    y = np.sin(x)+x/6 + np.random.randn(n)/10\n",
-    "    return(x,y)"
+    "    import pandas as pd\n",
+    "    df = pd.read_csv('stock.txt')\n",
+    "    return(df['time'].values,df['price'])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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kzoYh3BvN9tslfVnSpyLie3XXUwXbH5B0NiKO1F1LDXZKuk7SAxFxraT/0oD/eT4M8vnl\nA8o+3H5c0tts/1q9VdUnsssUB3qp4jCE+2lJe9ds78nbkmf7CmXB3omIp+qup0I3SrrV9qvKpuF+\nwfZf1VtSZZYkLUXE6r/SDikL+9T9oqR/jYjliPhfSU9J+rmaa6rav9u+WpLyx7OD3NkwhPsLkvbZ\nvsb2lcpOshyuuaaBs21l864nIuLzdddTpYj4vYjYExEtZf+/vx4RjRjFRcR3JZ2y/e686f2SjtdY\nUlUWJd1gezz/s/9+NeBEco/Dku7In98h6W8GubPa11CNiPO275L0jLIz6A9HxLGay6rCjZI+Iuk7\ntl/M2z4TEU/XWBOq8VuSOvlg5qSkj9dcz8BFxPO2D0k6quxKsW8p4btVbT8u6SZJu2wvSfqspD+U\n9KTtO5V9I+6HB1oDd6gCQHqGYVoGAFAywh0AEkS4A0CCCHcASBDhDgAJItwBIEGEOwAkiHAHgAT9\nH/Tghco0V2JXAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x105421080>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "import pandas as pd\n",
     "import numpy as np\n",
@@ -67,30 +53,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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S/iqfknrQ9lvqLqrXIuK0pD+RNC/pjKRzEfEP9VZVuXdGxJn8+fclvbOXO+uHcG8022+V\n9FVJn4mIH9RdTxVsf1DS2YiYqbuWGmyXdK2kByLiGkn/pR7/9bwf5PPLB5R9ub1L0ltsf6TequoT\n2WWKPb1UsR/C/bSk3Su2d+VtybN9hbJgb0fEU3XXU6EbJd1i+xVl03C/aPvL9ZZUmQVJCxGx/Le0\nQ8rCPnW/JOlfI2IxIv5X0lOSfr7mmqr277avlqT88Wwvd9YP4f68pL2299i+UtlJlsM119Rztq1s\n3vVERHyh7nqqFBG/HxG7ImJc2b/vb0REI0ZxEfF9Sadsvztv+oCk4zWWVJV5STfYHs7/2/+AGnAi\neY3Dkm7Pn98u6W96ubPa11CNiPO275T0jLIz6A9HxLGay6rCjZI+Kul7tl/I2z4XEU/XWBOq8duS\n2vlg5qSkT9RcT89FxBHbhyQdVXal2HeU8N2qth+XdJOkHbYXJN0r6Y8kPWn7DmW/iPsbPa2BO1QB\nID39MC0DACgZ4Q4ACSLcASBBhDsAJIhwB4AEEe4AkCDCHQASRLgDQIL+D6NxjQ1q3TKAAAAAAElF\nTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x1050e9828>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "(0.0, -0.47808641737141788)"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# let's make a model that always predict the mean\n",
     "# we need to do a reshape of the data because train_test_split expects a [[]] and the data is just one column []\n",
@@ -120,30 +85,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x10a740240>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "(0.42924577812346643, -0.45237104233936654)"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# let's do a linear regression with a polynomial of degree = 1 ( a line)\n",
     "def question2():\n",
@@ -171,30 +115,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x105408a90>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "(0.45109980444082465, -0.068569841499159789)"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# let's do a polynomial 2 regression\n",
     "def question3():\n",
@@ -224,30 +147,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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PvsG3UVUUXaysqG3k5sd8bX+P3jiRlJ62/UX7DpTiSl0GtzEmFvgD8AVgLHC1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\nWmp8x+wgFc8AAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x10a78e710>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "(0.9999997643602121, -38.876365895794137)"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# let's do a linear regression for a polynomial of grade 10\n",
     "# return r2 scores for the training and testing set\n",
@@ -279,30 +181,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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xk4Rg+DEpmPQx9xBetQpLbp1Dx45FHz0gX2Fhfp9ofFmi6QRsM8b8bozJBD4C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\nalzL7+OSRQAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x10ac2a5c0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "6"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# let's do a for loop from degree 2 through 9 for the data\n",
     "# capture the r2 scores for the test and training sets for each iteration\n",
@@ -349,6 +230,13 @@
     "\n",
     "question5()"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
diff --git a/week1/exercise3.ipynb b/week1/exercise3.ipynb
index a44c869..8015930 100644
--- a/week1/exercise3.ipynb
+++ b/week1/exercise3.ipynb
@@ -13,14 +13,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "def getDataSet1():\n",
     "    import pandas as pd\n",
     "\n",
-    "    df = pd.read_csv('ex2data1.txt',header=None)\n",
+    "    df = pd.read_csv('scores.txt',header=None)\n",
     "    df.columns = ['test1','test2','pass']\n",
     "\n",
     "    X = df[['test1','test2']]\n",
@@ -31,14 +31,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "def getDataSet2():\n",
     "    import pandas as pd\n",
     "\n",
-    "    df = pd.read_csv('ex2data2.txt',header=None)\n",
+    "    df = pd.read_csv('transistor.txt',header=None)\n",
     "    df.columns = ['test1','test2','pass']\n",
     "    \n",
     "    X = df[['test1','test2']]\n",
@@ -55,7 +55,7 @@
     "def plotData():\n",
     "    import matplotlib.pyplot as plt\n",
     "    \n",
-    "    (X,y,df) = getDataSet1()\n",
+    "    (X,y,df) = getDataSet2()\n",
     "    notPassed = df[df['pass'] == 0]\n",
     "    passed = df[df['pass'] == 1]\n",
     "    \n",
@@ -69,7 +69,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -101,30 +101,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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sRuA3qfXeugm4y8yuI8z3+S+AzzZtazfOzwHXJx+jwCMdvZK7B/0BLAFeAH6B\n2uKBvmT7J4D/VfT4msZ6TfKfcwfwFGARjPkAMNC07VVgdfL1auDVosfZNL4PAT8hucYUy7gbxvnL\nwN+EPmZqnWcPASuoraJ/CvhXIf9NA78KPNbw+D8Cvx/q+wysB/Y3PG45TuBRYLjV8xbyEeqMPlUL\n5AL9CbU/qvPJ437CH7MDz5jZXjMbTbatcvejyddvAauKGVpb1wLHgD9P0mTfMLOlhD/uui8A9ds+\nBTtmdz8C/DFwEDgKvAPsJey/6f3AkJn1m9kS4E5gDQG/z03ajbN+0K3r6H0PNtB7ihbIRTCzu4Bp\nd99b9Fg69Ivufgu1U8MvmtmnGr/ptelDaKVZfcAtwCPu/i+AUzSdigc6bpJ89ueB7zR/L7QxJ/nh\nu6kdWD8MLOXSVENQ3P0VaqmlZ4DvAS8C55qeE9T73E4vxxlsoK9z9xlqHTEvtEBOvhVaC+RPAp83\nswPAt6ilbx4m7DHXZ224+zS1nPGtwNtmthog+Txd3AhbOgwcdvfnk8e7qQX+0McNtQPqC+7+dvI4\n5DF/GviJux9z938C/ora33nof9OPufvH3f1T1K4hvEbY73OjduM8Qu3MpK6j9z3IQG8RtkB296+4\n+zXuvp7aqfn/dvcRAh6zmS01syvqX1PLHe8HnqQ2VghszADu/hZwyMxuSDZtBF4m8HEnhrmYtoGw\nx3wQuM3MlpiZcfF9DvZvGsDMViaf1wK/AvwlYb/PjdqN80ng15Pqm9uAdxpSPPMr+mJEmwsU/5xa\ni+MfUQs8f5Bs/zngb4E3qJ36vr/osbYZ/+3AU6GPORnbD5OPl4CtyfZ+aheVXwf+GlhR9FhbjP1m\nYDL5G/kfwJWhj5ta6uME8KGGbaGP+Q+BHyf74TeB94f8N52MeYLaAemHwMZQ32dqB/yjwD9RO0u9\nv904qRV2/Fdq1yr3UauEWvBraWWsiEjJBZm6ERGR3lGgFxEpOQV6EZGSU6AXESk5BXoRkZJToBcR\nKTkFehGRklOgFxEpuf8PdxFNSbH6ry8AAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x1079a97f0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "(0.61333333333333329, 0.56000000000000005)"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "#default is a stratified solution for dummy classifier\n",
     "def getDummyClassifier():\n",
@@ -144,30 +123,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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XqZGZpQa7nt7POU/sZtfT+5lZalQ9JAmANkyJ1ITaQEs3fWf0ZvZmM/s7M/uB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\nB1kuE8THYozPAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x103fe0588>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "(0.94666666666666666, 0.92000000000000004)"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# change the n_neighbors parameter between 1 and 4 for testset 1 and 2, notice the\n",
     "# different graph and results\n",
@@ -191,30 +149,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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7gd8WkVuAFeBF4MPRsS+KyO9TSS4Ad1cboouocbh/p6Op0yyGJ6m68PEu0Pgpz9HYcaXt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\nMRhjjKljicEYY0wdSwzGGGPq/H+n27Xw3ahdTQAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x107d4a550>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "(1.0, 0.73333333333333328)"
-      ]
-     },
-     "execution_count": 40,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# change the n_neighbors parameter between 1 and 4 for testset 1 and 2, notice the\n",
     "# different graph and results\n",
@@ -225,7 +162,7 @@
     "    from sklearn.model_selection import train_test_split\n",
     "    \n",
     "    depth = 0\n",
-    "    (X,y,df) = getDataSet2()\n",
+    "    (X,y,df) = getDataSet1()\n",
     "    \n",
     "    X_train, X_test, y_train, y_test = train_test_split(X,y, random_state=0)\n",
     "#     tree = DecisionTreeClassifier(max_depth = depth, random_state=0).fit(X_train,y_train)\n",
@@ -238,30 +175,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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H5R48hwQghjdTM2Xu1dJN7XX9uoNmYkxrhb4D2UappTzSTr0I9BOcT9sA3A/cmXx+J3Bf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\nxMUAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x107add4a8>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "(1.0, 0.56000000000000005)"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "#run for set1 and set2, notice the difference\n",
     "def getSVC():\n",
@@ -280,40 +196,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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D7MRaqzu6PbeM+frQd+b9zhNB9ueQ0kjdTLGStgG4D9gbf78XuDeF15AATIyeGmh7KKp8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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x103f622b0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "SVC(C=1, cache_size=200, class_weight=None, coef0=0.0,\n",
-      "  decision_function_shape='ovr', degree=3, gamma='auto', kernel='linear',\n",
-      "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
-      "  tol=0.001, verbose=False)\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "(0.90666666666666662, 0.88)"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "#run for set1 and set2, notice the difference\n",
     "def getSVCGridSearch():\n",
@@ -322,7 +207,7 @@
     "    from sklearn.svm import SVC\n",
     "    from sklearn.model_selection import GridSearchCV\n",
     "    \n",
-    "    (X,y,df) = getDataSet1()\n",
+    "    (X,y,df) = getDataSet2()\n",
     "    X_train, X_test, y_train, y_test = train_test_split(X,y, random_state=0)\n",
     "    parameters = {'kernel':('linear', 'rbf'), 'C':[1,5,10,20,50]}\n",
     "    clf = GridSearchCV(SVC(), parameters)\n",
@@ -337,23 +222,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Nearest Neighbor: \n",
-      " [[ 1.          0.        ]\n",
-      " [ 0.66666667  0.33333333]\n",
-      " [ 0.          1.        ]\n",
-      " [ 0.          1.        ]]\n",
-      "SVC: \n",
-      " [0 0 0 1]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# with your best nearest neighbor for dataset1 calculate the probability that \n",
     "# a student that gets 55 and 55, and 60 and 60 will pass, how about 65 and 65? \n",
diff --git a/week1/poly.txt b/week1/poly.txt
deleted file mode 100644
index 480b280..0000000
--- a/week1/poly.txt
+++ /dev/null
@@ -1,41 +0,0 @@
-index,time,price
-0,0.588017448655888,0.4012007092371197
-1,0.38979599253266417,0.036592403976009436
-2,0.8390665075224258,0.4549518297227998
-3,1.5161951689645883,2.154166581813321
-4,1.6481603556910147,1.3765116204708003
-5,0.956291988759145,0.9897985520001106
-6,1.855157677636735,1.1606284571004675
-7,1.7444193921058955,1.8256019235138357
-8,2.0168757673508653,1.0364714591997655
-9,2.444558475005098,1.385885870190063
-10,2.6121170878228566,1.0772976283522726
-11,3.305270656167145,1.0677762393585728
-12,3.330602318638741,0.7520461042835364
-13,3.3738916721642758,0.5008710998089909
-14,3.7376980006587313,0.6750814540244078
-15,3.957378621945268,0.7336390528694392
-16,4.600590460283304,0.5619455088682535
-17,4.290588271052425,0.6186668539778448
-18,4.719740515934915,0.3618331631670876
-19,4.5870962920276295,0.41595724145889945
-20,4.2772085229271015,0.29478871083299596
-21,5.602488249762171,1.1183101453632633
-22,5.929171040645476,1.3586756592097105
-23,5.650047557300416,0.7162444098601607
-24,6.91043102850869,2.9495360501055985
-25,5.925467852056822,1.4910918809959142
-26,6.681919505767148,2.072159203273186
-27,6.860682306401644,2.987083409602744
-28,7.690413584273331,3.247690951754247
-29,7.925683692530864,3.6566402046440563
-30,7.743956834206664,3.818302507302736
-31,8.074772121918672,3.710309647449404
-32,7.9091996225848336,4.014675976560307
-33,7.8012729721304845,3.1874269373129747
-34,8.601978001506666,3.7344930337079063
-35,9.026475297393635,3.16840950037122
-36,9.640866124345138,2.7089459131492193
-37,9.887972770107623,2.6562324817764265
-38,9.614480804453759,2.9124090128933955
-39,9.899232416474888,2.8616126889918125
diff --git a/week1/ex2data1.txt b/week1/scores.txt
similarity index 100%
rename from week1/ex2data1.txt
rename to week1/scores.txt
diff --git a/week1/stock.txt b/week1/stock.txt
new file mode 100644
index 0000000..bb053c3
--- /dev/null
+++ b/week1/stock.txt
@@ -0,0 +1,16 @@
+time,price
+0.35281047,0.43770571
+0.79431716,0.99517935
+1.62431903,1.24877201
+2.59103578,0.98630796
+3.23065446,0.36408873
+3.375973,0.07512287  
+4.47573197,-0.16081
+4.96972856,-0.05233879
+5.69364194,0.3187423
+6.51069113,1.53763897
+7.17166586,1.82595557
+8.14799756,2.31966323
+8.72363612,2.08031157
+9.31004929,1.81942995
+10.08877265,1.21213026
\ No newline at end of file
diff --git a/week1/ex2data2.txt b/week1/transistor.txt
similarity index 100%
rename from week1/ex2data2.txt
rename to week1/transistor.txt