tsb95
/
MLProjects
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Clean up code and add comments

This commit is contained in:
tsb1995 2020-06-07 11:06:45 -07:00
parent b8ba9c81e6
commit 34ea348f9c
12 changed files with 3630 additions and 238 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

160
breastCancerML/.ipynb_checkpoints/breastCancerML-checkpoint.ipynb
View File

@ -10,7 +10,6 @@
"import numpy as np\n",
"import sklearn\n",
"from sklearn import svm, datasets, metrics\n",
"from sklearn.neighbors import KNeighborsClassifier\n",
"import pickle"
]
},
@ -18,93 +17,19 @@
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Features: ['mean radius' 'mean texture' 'mean perimeter' 'mean area'\n",
" 'mean smoothness' 'mean compactness' 'mean concavity'\n",
" 'mean concave points' 'mean symmetry' 'mean fractal dimension'\n",
" 'radius error' 'texture error' 'perimeter error' 'area error'\n",
" 'smoothness error' 'compactness error' 'concavity error'\n",
" 'concave points error' 'symmetry error' 'fractal dimension error'\n",
" 'worst radius' 'worst texture' 'worst perimeter' 'worst area'\n",
" 'worst smoothness' 'worst compactness' 'worst concavity'\n",
" 'worst concave points' 'worst symmetry' 'worst fractal dimension']\n",
"labels: ['malignant' 'benign']\n",
"data: [[1.799e+01 1.038e+01 1.228e+02 ... 2.654e-01 4.601e-01 1.189e-01]\n",
" [2.057e+01 1.777e+01 1.329e+02 ... 1.860e-01 2.750e-01 8.902e-02]\n",
" [1.969e+01 2.125e+01 1.300e+02 ... 2.430e-01 3.613e-01 8.758e-02]\n",
" ...\n",
" [1.660e+01 2.808e+01 1.083e+02 ... 1.418e-01 2.218e-01 7.820e-02]\n",
" [2.060e+01 2.933e+01 1.401e+02 ... 2.650e-01 4.087e-01 1.240e-01]\n",
" [7.760e+00 2.454e+01 4.792e+01 ... 0.000e+00 2.871e-01 7.039e-02]]\n",
"target: [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 1 0 0 0 0 0 0 0 0 1 0 1 1 1 1 1 0 0 1 0 0 1 1 1 1 0 1 0 0 1 1 1 1 0 1 0 0\n",
" 1 0 1 0 0 1 1 1 0 0 1 0 0 0 1 1 1 0 1 1 0 0 1 1 1 0 0 1 1 1 1 0 1 1 0 1 1\n",
" 1 1 1 1 1 1 0 0 0 1 0 0 1 1 1 0 0 1 0 1 0 0 1 0 0 1 1 0 1 1 0 1 1 1 1 0 1\n",
" 1 1 1 1 1 1 1 1 0 1 1 1 1 0 0 1 0 1 1 0 0 1 1 0 0 1 1 1 1 0 1 1 0 0 0 1 0\n",
" 1 0 1 1 1 0 1 1 0 0 1 0 0 0 0 1 0 0 0 1 0 1 0 1 1 0 1 0 0 0 0 1 1 0 0 1 1\n",
" 1 0 1 1 1 1 1 0 0 1 1 0 1 1 0 0 1 0 1 1 1 1 0 1 1 1 1 1 0 1 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0 0 1 1 1 1 1 1 0 1 0 1 1 0 1 1 0 1 0 0 1 1 1 1 1 1 1 1 1 1 1 1\n",
" 1 0 1 1 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 0 1 0 1 1 1 1 0 0 0 1 1\n",
" 1 1 0 1 0 1 0 1 1 1 0 1 1 1 1 1 1 1 0 0 0 1 1 1 1 1 1 1 1 1 1 1 0 0 1 0 0\n",
" 0 1 0 0 1 1 1 1 1 0 1 1 1 1 1 0 1 1 1 0 1 1 0 0 1 1 1 1 1 1 0 1 1 1 1 1 1\n",
" 1 0 1 1 1 1 1 0 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 0 1 0 0 1 0 1 1 1 1 1 0 1 1\n",
" 0 1 0 1 1 0 1 0 1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 0 1\n",
" 1 1 1 1 1 1 0 1 0 1 1 0 1 1 1 1 1 0 0 1 0 1 0 1 1 1 1 1 0 1 1 0 1 0 1 0 0\n",
" 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 0 1 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
" 1 1 1 1 1 1 1 0 0 0 0 0 0 1]\n"
]
}
],
"outputs": [],
"source": [
"cancer = datasets.load_breast_cancer()\n",
"print(\"Features:\", cancer.feature_names)\n",
"print(\"labels:\", cancer.target_names)\n",
"print(\"data:\", cancer.data)\n",
"print(\"target:\", cancer.target)"
"# Load in our data\n",
"cancer = datasets.load_breast_cancer()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[1.160e+01 2.449e+01 7.423e+01 4.172e+02 7.474e-02 5.688e-02 1.974e-02\n",
" 1.313e-02 1.935e-01 5.878e-02 2.512e-01 1.786e+00 1.961e+00 1.821e+01\n",
" 6.122e-03 2.337e-02 1.596e-02 6.998e-03 3.194e-02 2.211e-03 1.244e+01\n",
" 3.162e+01 8.139e+01 4.765e+02 9.545e-02 1.361e-01 7.239e-02 4.815e-02\n",
" 3.244e-01 6.745e-02]\n",
" [1.288e+01 1.822e+01 8.445e+01 4.931e+02 1.218e-01 1.661e-01 4.825e-02\n",
" 5.303e-02 1.709e-01 7.253e-02 4.426e-01 1.169e+00 3.176e+00 3.437e+01\n",
" 5.273e-03 2.329e-02 1.405e-02 1.244e-02 1.816e-02 3.299e-03 1.505e+01\n",
" 2.437e+01 9.931e+01 6.747e+02 1.456e-01 2.961e-01 1.246e-01 1.096e-01\n",
" 2.582e-01 8.893e-02]\n",
" [1.086e+01 2.148e+01 6.851e+01 3.605e+02 7.431e-02 4.227e-02 0.000e+00\n",
" 0.000e+00 1.661e-01 5.948e-02 3.163e-01 1.304e+00 2.115e+00 2.067e+01\n",
" 9.579e-03 1.104e-02 0.000e+00 0.000e+00 3.004e-02 2.228e-03 1.166e+01\n",
" 2.477e+01 7.408e+01 4.123e+02 1.001e-01 7.348e-02 0.000e+00 0.000e+00\n",
" 2.458e-01 6.592e-02]\n",
" [2.020e+01 2.683e+01 1.337e+02 1.234e+03 9.905e-02 1.669e-01 1.641e-01\n",
" 1.265e-01 1.875e-01 6.020e-02 9.761e-01 1.892e+00 7.128e+00 1.036e+02\n",
" 8.439e-03 4.674e-02 5.904e-02 2.536e-02 3.710e-02 4.286e-03 2.419e+01\n",
" 3.381e+01 1.600e+02 1.671e+03 1.278e-01 3.416e-01 3.703e-01 2.152e-01\n",
" 3.271e-01 7.632e-02]\n",
" [2.047e+01 2.067e+01 1.347e+02 1.299e+03 9.156e-02 1.313e-01 1.523e-01\n",
" 1.015e-01 2.166e-01 5.419e-02 8.336e-01 1.736e+00 5.168e+00 1.004e+02\n",
" 4.938e-03 3.089e-02 4.093e-02 1.699e-02 2.816e-02 2.719e-03 2.323e+01\n",
" 2.715e+01 1.520e+02 1.645e+03 1.097e-01 2.534e-01 3.092e-01 1.613e-01\n",
" 3.220e-01 6.386e-02]]\n"
]
}
],
"outputs": [],
"source": [
"# Split our input and target features\n",
"x = cancer.data\n",
"y = cancer.target\n",
"\n",
@ -113,9 +38,7 @@
" x,y,test_size=0.1)\n",
"\n",
"x_test, x_dev, y_test, y_dev = sklearn.model_selection.train_test_split(\n",
" x_test, y_test, test_size=0.5)\n",
"\n",
"print(x_train[:5])"
" x_test, y_test, test_size=0.5)"
]
},
{
@ -127,15 +50,17 @@
"name": "stdout",
"output_type": "stream",
"text": [
"0.9285714285714286\n"
"0.9642857142857143\n"
]
}
],
"source": [
"# Setup Support Vector Classifier\n",
"classes = cancer.target_names\n",
"clf = svm.SVC(kernel='linear', gamma='scale')\n",
"clf.fit(x_train, y_train)\n",
"\n",
"# Make predictions and measure accuracy\n",
"y_pred = clf.predict(x_test)\n",
"acc = metrics.accuracy_score(y_test, y_pred)\n",
"print(acc)"
@ -152,45 +77,43 @@
"text": [
"0.9310344827586207\n",
"0.9655172413793104\n",
"0.9310344827586207\n",
"0.896551724137931\n",
"0.896551724137931\n",
"0.896551724137931\n",
"0.896551724137931\n",
"0.896551724137931\n",
"0.9310344827586207\n",
"0.9310344827586207\n",
"0.6896551724137931\n",
"0.896551724137931\n",
"0.9310344827586207\n",
"0.896551724137931\n",
"0.9310344827586207\n",
"0.9310344827586207\n",
"0.9310344827586207\n",
"0.9655172413793104\n",
"0.9655172413793104\n",
"0.9655172413793104\n",
"0.6896551724137931\n",
"0.6896551724137931\n",
"0.6896551724137931\n",
"0.9310344827586207\n",
"0.9310344827586207\n",
"0.9310344827586207\n",
"0.9310344827586207\n",
"0.9310344827586207\n",
"0.9310344827586207\n",
"0.9310344827586207\n",
"0.6206896551724138\n",
"0.7931034482758621\n",
"0.8620689655172413\n",
"0.8620689655172413\n",
"0.8275862068965517\n",
"0.8620689655172413\n",
"0.8620689655172413\n",
"0.896551724137931\n",
"0.9310344827586207\n",
"0.9310344827586207\n",
"0.6206896551724138\n",
"0.6206896551724138\n",
"0.6206896551724138\n",
"0.5862068965517241\n",
"0.5517241379310345\n",
"0.4827586206896552\n",
"0.41379310344827586\n",
"0.41379310344827586\n",
"0.41379310344827586\n",
"0.41379310344827586\n",
"0.41379310344827586\n",
"0.41379310344827586\n"
"0.4827586206896552\n",
"0.4827586206896552\n",
"0.4827586206896552\n",
"0.4827586206896552\n"
]
}
],
"source": [
"# Tune parameters of kernel, C, gamma \n",
"# Note: use logarithmic scale random values\n",
"# Note: use logarithmically scaled values for C\n",
"kernels = ['linear','rbf','sigmoid']\n",
"C_values = [0.001, 0.01, 0.1, 1, 5, 25, 50, 100, 500, 1000]\n",
"\n",
"\n",
"\n",
"best = 0\n",
"for kernel in kernels:\n",
" for C in C_values:\n",
@ -203,25 +126,24 @@
" if acc > best:\n",
" best = acc\n",
" with open('cancerModel.pickle','wb') as f:\n",
" pickle.dump(clf,f)\n",
" \n",
"\n"
" pickle.dump(clf,f)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.9285714285714286\n"
"0.9642857142857143\n"
]
}
],
"source": [
"# Load in our best model (according to Validation accuracy)\n",
"pickle_in = open('cancerModel.pickle','rb')\n",
"clf = pickle.load(pickle_in)\n",
"y_pred = clf.predict(x_test)\n",
@ -253,7 +175,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.4"
"version": "3.7.5"
}
},
"nbformat": 4,

160
breastCancerML/breastCancerML.ipynb
View File

@ -10,7 +10,6 @@
"import numpy as np\n",
"import sklearn\n",
"from sklearn import svm, datasets, metrics\n",
"from sklearn.neighbors import KNeighborsClassifier\n",
"import pickle"
]
},
@ -18,93 +17,19 @@
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Features: ['mean radius' 'mean texture' 'mean perimeter' 'mean area'\n",
" 'mean smoothness' 'mean compactness' 'mean concavity'\n",
" 'mean concave points' 'mean symmetry' 'mean fractal dimension'\n",
" 'radius error' 'texture error' 'perimeter error' 'area error'\n",
" 'smoothness error' 'compactness error' 'concavity error'\n",
" 'concave points error' 'symmetry error' 'fractal dimension error'\n",
" 'worst radius' 'worst texture' 'worst perimeter' 'worst area'\n",
" 'worst smoothness' 'worst compactness' 'worst concavity'\n",
" 'worst concave points' 'worst symmetry' 'worst fractal dimension']\n",
"labels: ['malignant' 'benign']\n",
"data: [[1.799e+01 1.038e+01 1.228e+02 ... 2.654e-01 4.601e-01 1.189e-01]\n",
" [2.057e+01 1.777e+01 1.329e+02 ... 1.860e-01 2.750e-01 8.902e-02]\n",
" [1.969e+01 2.125e+01 1.300e+02 ... 2.430e-01 3.613e-01 8.758e-02]\n",
" ...\n",
" [1.660e+01 2.808e+01 1.083e+02 ... 1.418e-01 2.218e-01 7.820e-02]\n",
" [2.060e+01 2.933e+01 1.401e+02 ... 2.650e-01 4.087e-01 1.240e-01]\n",
" [7.760e+00 2.454e+01 4.792e+01 ... 0.000e+00 2.871e-01 7.039e-02]]\n",
"target: [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 1 0 0 0 0 0 0 0 0 1 0 1 1 1 1 1 0 0 1 0 0 1 1 1 1 0 1 0 0 1 1 1 1 0 1 0 0\n",
" 1 0 1 0 0 1 1 1 0 0 1 0 0 0 1 1 1 0 1 1 0 0 1 1 1 0 0 1 1 1 1 0 1 1 0 1 1\n",
" 1 1 1 1 1 1 0 0 0 1 0 0 1 1 1 0 0 1 0 1 0 0 1 0 0 1 1 0 1 1 0 1 1 1 1 0 1\n",
" 1 1 1 1 1 1 1 1 0 1 1 1 1 0 0 1 0 1 1 0 0 1 1 0 0 1 1 1 1 0 1 1 0 0 0 1 0\n",
" 1 0 1 1 1 0 1 1 0 0 1 0 0 0 0 1 0 0 0 1 0 1 0 1 1 0 1 0 0 0 0 1 1 0 0 1 1\n",
" 1 0 1 1 1 1 1 0 0 1 1 0 1 1 0 0 1 0 1 1 1 1 0 1 1 1 1 1 0 1 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0 0 1 1 1 1 1 1 0 1 0 1 1 0 1 1 0 1 0 0 1 1 1 1 1 1 1 1 1 1 1 1\n",
" 1 0 1 1 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 0 1 0 1 1 1 1 0 0 0 1 1\n",
" 1 1 0 1 0 1 0 1 1 1 0 1 1 1 1 1 1 1 0 0 0 1 1 1 1 1 1 1 1 1 1 1 0 0 1 0 0\n",
" 0 1 0 0 1 1 1 1 1 0 1 1 1 1 1 0 1 1 1 0 1 1 0 0 1 1 1 1 1 1 0 1 1 1 1 1 1\n",
" 1 0 1 1 1 1 1 0 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 0 1 0 0 1 0 1 1 1 1 1 0 1 1\n",
" 0 1 0 1 1 0 1 0 1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 0 1\n",
" 1 1 1 1 1 1 0 1 0 1 1 0 1 1 1 1 1 0 0 1 0 1 0 1 1 1 1 1 0 1 1 0 1 0 1 0 0\n",
" 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 0 1 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
" 1 1 1 1 1 1 1 0 0 0 0 0 0 1]\n"
]
}
],
"outputs": [],
"source": [
"cancer = datasets.load_breast_cancer()\n",
"print(\"Features:\", cancer.feature_names)\n",
"print(\"labels:\", cancer.target_names)\n",
"print(\"data:\", cancer.data)\n",
"print(\"target:\", cancer.target)"
"# Load in our data\n",
"cancer = datasets.load_breast_cancer()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[1.160e+01 2.449e+01 7.423e+01 4.172e+02 7.474e-02 5.688e-02 1.974e-02\n",
" 1.313e-02 1.935e-01 5.878e-02 2.512e-01 1.786e+00 1.961e+00 1.821e+01\n",
" 6.122e-03 2.337e-02 1.596e-02 6.998e-03 3.194e-02 2.211e-03 1.244e+01\n",
" 3.162e+01 8.139e+01 4.765e+02 9.545e-02 1.361e-01 7.239e-02 4.815e-02\n",
" 3.244e-01 6.745e-02]\n",
" [1.288e+01 1.822e+01 8.445e+01 4.931e+02 1.218e-01 1.661e-01 4.825e-02\n",
" 5.303e-02 1.709e-01 7.253e-02 4.426e-01 1.169e+00 3.176e+00 3.437e+01\n",
" 5.273e-03 2.329e-02 1.405e-02 1.244e-02 1.816e-02 3.299e-03 1.505e+01\n",
" 2.437e+01 9.931e+01 6.747e+02 1.456e-01 2.961e-01 1.246e-01 1.096e-01\n",
" 2.582e-01 8.893e-02]\n",
" [1.086e+01 2.148e+01 6.851e+01 3.605e+02 7.431e-02 4.227e-02 0.000e+00\n",
" 0.000e+00 1.661e-01 5.948e-02 3.163e-01 1.304e+00 2.115e+00 2.067e+01\n",
" 9.579e-03 1.104e-02 0.000e+00 0.000e+00 3.004e-02 2.228e-03 1.166e+01\n",
" 2.477e+01 7.408e+01 4.123e+02 1.001e-01 7.348e-02 0.000e+00 0.000e+00\n",
" 2.458e-01 6.592e-02]\n",
" [2.020e+01 2.683e+01 1.337e+02 1.234e+03 9.905e-02 1.669e-01 1.641e-01\n",
" 1.265e-01 1.875e-01 6.020e-02 9.761e-01 1.892e+00 7.128e+00 1.036e+02\n",
" 8.439e-03 4.674e-02 5.904e-02 2.536e-02 3.710e-02 4.286e-03 2.419e+01\n",
" 3.381e+01 1.600e+02 1.671e+03 1.278e-01 3.416e-01 3.703e-01 2.152e-01\n",
" 3.271e-01 7.632e-02]\n",
" [2.047e+01 2.067e+01 1.347e+02 1.299e+03 9.156e-02 1.313e-01 1.523e-01\n",
" 1.015e-01 2.166e-01 5.419e-02 8.336e-01 1.736e+00 5.168e+00 1.004e+02\n",
" 4.938e-03 3.089e-02 4.093e-02 1.699e-02 2.816e-02 2.719e-03 2.323e+01\n",
" 2.715e+01 1.520e+02 1.645e+03 1.097e-01 2.534e-01 3.092e-01 1.613e-01\n",
" 3.220e-01 6.386e-02]]\n"
]
}
],
"outputs": [],
"source": [
"# Split our input and target features\n",
"x = cancer.data\n",
"y = cancer.target\n",
"\n",
@ -113,9 +38,7 @@
" x,y,test_size=0.1)\n",
"\n",
"x_test, x_dev, y_test, y_dev = sklearn.model_selection.train_test_split(\n",
" x_test, y_test, test_size=0.5)\n",
"\n",
"print(x_train[:5])"
" x_test, y_test, test_size=0.5)"
]
},
{
@ -127,15 +50,17 @@
"name": "stdout",
"output_type": "stream",
"text": [
"0.9285714285714286\n"
"0.9642857142857143\n"
]
}
],
"source": [
"# Setup Support Vector Classifier\n",
"classes = cancer.target_names\n",
"clf = svm.SVC(kernel='linear', gamma='scale')\n",
"clf.fit(x_train, y_train)\n",
"\n",
"# Make predictions and measure accuracy\n",
"y_pred = clf.predict(x_test)\n",
"acc = metrics.accuracy_score(y_test, y_pred)\n",
"print(acc)"
@ -152,45 +77,43 @@
"text": [
"0.9310344827586207\n",
"0.9655172413793104\n",
"0.9310344827586207\n",
"0.896551724137931\n",
"0.896551724137931\n",
"0.896551724137931\n",
"0.896551724137931\n",
"0.896551724137931\n",
"0.9310344827586207\n",
"0.9310344827586207\n",
"0.6896551724137931\n",
"0.896551724137931\n",
"0.9310344827586207\n",
"0.896551724137931\n",
"0.9310344827586207\n",
"0.9310344827586207\n",
"0.9310344827586207\n",
"0.9655172413793104\n",
"0.9655172413793104\n",
"0.9655172413793104\n",
"0.6896551724137931\n",
"0.6896551724137931\n",
"0.6896551724137931\n",
"0.9310344827586207\n",
"0.9310344827586207\n",
"0.9310344827586207\n",
"0.9310344827586207\n",
"0.9310344827586207\n",
"0.9310344827586207\n",
"0.9310344827586207\n",
"0.6206896551724138\n",
"0.7931034482758621\n",
"0.8620689655172413\n",
"0.8620689655172413\n",
"0.8275862068965517\n",
"0.8620689655172413\n",
"0.8620689655172413\n",
"0.896551724137931\n",
"0.9310344827586207\n",
"0.9310344827586207\n",
"0.6206896551724138\n",
"0.6206896551724138\n",
"0.6206896551724138\n",
"0.5862068965517241\n",
"0.5517241379310345\n",
"0.4827586206896552\n",
"0.41379310344827586\n",
"0.41379310344827586\n",
"0.41379310344827586\n",
"0.41379310344827586\n",
"0.41379310344827586\n",
"0.41379310344827586\n"
"0.4827586206896552\n",
"0.4827586206896552\n",
"0.4827586206896552\n",
"0.4827586206896552\n"
]
}
],
"source": [
"# Tune parameters of kernel, C, gamma \n",
"# Note: use logarithmic scale random values\n",
"# Note: use logarithmically scaled values for C\n",
"kernels = ['linear','rbf','sigmoid']\n",
"C_values = [0.001, 0.01, 0.1, 1, 5, 25, 50, 100, 500, 1000]\n",
"\n",
"\n",
"\n",
"best = 0\n",
"for kernel in kernels:\n",
" for C in C_values:\n",
@ -203,25 +126,24 @@
" if acc > best:\n",
" best = acc\n",
" with open('cancerModel.pickle','wb') as f:\n",
" pickle.dump(clf,f)\n",
" \n",
"\n"
" pickle.dump(clf,f)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.9285714285714286\n"
"0.9642857142857143\n"
]
}
],
"source": [
"# Load in our best model (according to Validation accuracy)\n",
"pickle_in = open('cancerModel.pickle','rb')\n",
"clf = pickle.load(pickle_in)\n",
"y_pred = clf.predict(x_test)\n",
@ -253,7 +175,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.4"
"version": "3.7.5"
}
},
"nbformat": 4,

BIN
breastCancerML/cancerModel.pickle
View File

Binary file not shown.

1225
kaggle_time_series_air_passenger/.ipynb_checkpoints/model-checkpoint.ipynb Normal file
View File

File diff suppressed because one or more lines are too long

404
kaggle_time_series_air_passenger/.ipynb_checkpoints/model2-checkpoint.ipynb Normal file
View File

File diff suppressed because one or more lines are too long

145
kaggle_time_series_air_passenger/AirPassengers.csv Normal file
View File

@ -0,0 +1,145 @@
Month,#Passengers
1949-01,112
1949-02,118
1949-03,132
1949-04,129
1949-05,121
1949-06,135
1949-07,148
1949-08,148
1949-09,136
1949-10,119
1949-11,104
1949-12,118
1950-01,115
1950-02,126
1950-03,141
1950-04,135
1950-05,125
1950-06,149
1950-07,170
1950-08,170
1950-09,158
1950-10,133
1950-11,114
1950-12,140
1951-01,145
1951-02,150
1951-03,178
1951-04,163
1951-05,172
1951-06,178
1951-07,199
1951-08,199
1951-09,184
1951-10,162
1951-11,146
1951-12,166
1952-01,171
1952-02,180
1952-03,193
1952-04,181
1952-05,183
1952-06,218
1952-07,230
1952-08,242
1952-09,209
1952-10,191
1952-11,172
1952-12,194
1953-01,196
1953-02,196
1953-03,236
1953-04,235
1953-05,229
1953-06,243
1953-07,264
1953-08,272
1953-09,237
1953-10,211
1953-11,180
1953-12,201
1954-01,204
1954-02,188
1954-03,235
1954-04,227
1954-05,234
1954-06,264
1954-07,302
1954-08,293
1954-09,259
1954-10,229
1954-11,203
1954-12,229
1955-01,242
1955-02,233
1955-03,267
1955-04,269
1955-05,270
1955-06,315
1955-07,364
1955-08,347
1955-09,312
1955-10,274
1955-11,237
1955-12,278
1956-01,284
1956-02,277
1956-03,317
1956-04,313
1956-05,318
1956-06,374
1956-07,413
1956-08,405
1956-09,355
1956-10,306
1956-11,271
1956-12,306
1957-01,315
1957-02,301
1957-03,356
1957-04,348
1957-05,355
1957-06,422
1957-07,465
1957-08,467
1957-09,404
1957-10,347
1957-11,305
1957-12,336
1958-01,340
1958-02,318
1958-03,362
1958-04,348
1958-05,363
1958-06,435
1958-07,491
1958-08,505
1958-09,404
1958-10,359
1958-11,310
1958-12,337
1959-01,360
1959-02,342
1959-03,406
1959-04,396
1959-05,420
1959-06,472
1959-07,548
1959-08,559
1959-09,463
1959-10,407
1959-11,362
1959-12,405
1960-01,417
1960-02,391
1960-03,419
1960-04,461
1960-05,472
1960-06,535
1960-07,622
1960-08,606
1960-09,508
1960-10,461
1960-11,390
1960-12,432
1 Month #Passengers
2 1949-01 112
3 1949-02 118
4 1949-03 132
5 1949-04 129
6 1949-05 121
7 1949-06 135
8 1949-07 148
9 1949-08 148
10 1949-09 136
11 1949-10 119
12 1949-11 104
13 1949-12 118
14 1950-01 115
15 1950-02 126
16 1950-03 141
17 1950-04 135
18 1950-05 125
19 1950-06 149
20 1950-07 170
21 1950-08 170
22 1950-09 158
23 1950-10 133
24 1950-11 114
25 1950-12 140
26 1951-01 145
27 1951-02 150
28 1951-03 178
29 1951-04 163
30 1951-05 172
31 1951-06 178
32 1951-07 199
33 1951-08 199
34 1951-09 184
35 1951-10 162
36 1951-11 146
37 1951-12 166
38 1952-01 171
39 1952-02 180
40 1952-03 193
41 1952-04 181
42 1952-05 183
43 1952-06 218
44 1952-07 230
45 1952-08 242
46 1952-09 209
47 1952-10 191
48 1952-11 172
49 1952-12 194
50 1953-01 196
51 1953-02 196
52 1953-03 236
53 1953-04 235
54 1953-05 229
55 1953-06 243
56 1953-07 264
57 1953-08 272
58 1953-09 237
59 1953-10 211
60 1953-11 180
61 1953-12 201
62 1954-01 204
63 1954-02 188
64 1954-03 235
65 1954-04 227
66 1954-05 234
67 1954-06 264
68 1954-07 302
69 1954-08 293
70 1954-09 259
71 1954-10 229
72 1954-11 203
73 1954-12 229
74 1955-01 242
75 1955-02 233
76 1955-03 267
77 1955-04 269
78 1955-05 270
79 1955-06 315
80 1955-07 364
81 1955-08 347
82 1955-09 312
83 1955-10 274
84 1955-11 237
85 1955-12 278
86 1956-01 284
87 1956-02 277
88 1956-03 317
89 1956-04 313
90 1956-05 318
91 1956-06 374
92 1956-07 413
93 1956-08 405
94 1956-09 355
95 1956-10 306
96 1956-11 271
97 1956-12 306
98 1957-01 315
99 1957-02 301
100 1957-03 356
101 1957-04 348
102 1957-05 355
103 1957-06 422
104 1957-07 465
105 1957-08 467
106 1957-09 404
107 1957-10 347
108 1957-11 305
109 1957-12 336
110 1958-01 340
111 1958-02 318
112 1958-03 362
113 1958-04 348
114 1958-05 363
115 1958-06 435
116 1958-07 491
117 1958-08 505
118 1958-09 404
119 1958-10 359
120 1958-11 310
121 1958-12 337
122 1959-01 360
123 1959-02 342
124 1959-03 406
125 1959-04 396
126 1959-05 420
127 1959-06 472
128 1959-07 548
129 1959-08 559
130 1959-09 463
131 1959-10 407
132 1959-11 362
133 1959-12 405
134 1960-01 417
135 1960-02 391
136 1960-03 419
137 1960-04 461
138 1960-05 472
139 1960-06 535
140 1960-07 622
141 1960-08 606
142 1960-09 508
143 1960-10 461
144 1960-11 390
145 1960-12 432

145
kaggle_time_series_air_passenger/data/AirPassengers.csv Normal file
View File

@ -0,0 +1,145 @@
Month,#Passengers
1949-01,112
1949-02,118
1949-03,132
1949-04,129
1949-05,121
1949-06,135
1949-07,148
1949-08,148
1949-09,136
1949-10,119
1949-11,104
1949-12,118
1950-01,115
1950-02,126
1950-03,141
1950-04,135
1950-05,125
1950-06,149
1950-07,170
1950-08,170
1950-09,158
1950-10,133
1950-11,114
1950-12,140
1951-01,145
1951-02,150
1951-03,178
1951-04,163
1951-05,172
1951-06,178
1951-07,199
1951-08,199
1951-09,184
1951-10,162
1951-11,146
1951-12,166
1952-01,171
1952-02,180
1952-03,193
1952-04,181
1952-05,183
1952-06,218
1952-07,230
1952-08,242
1952-09,209
1952-10,191
1952-11,172
1952-12,194
1953-01,196
1953-02,196
1953-03,236
1953-04,235
1953-05,229
1953-06,243
1953-07,264
1953-08,272
1953-09,237
1953-10,211
1953-11,180
1953-12,201
1954-01,204
1954-02,188
1954-03,235
1954-04,227
1954-05,234
1954-06,264
1954-07,302
1954-08,293
1954-09,259
1954-10,229
1954-11,203
1954-12,229
1955-01,242
1955-02,233
1955-03,267
1955-04,269
1955-05,270
1955-06,315
1955-07,364
1955-08,347
1955-09,312
1955-10,274
1955-11,237
1955-12,278
1956-01,284
1956-02,277
1956-03,317
1956-04,313
1956-05,318
1956-06,374
1956-07,413
1956-08,405
1956-09,355
1956-10,306
1956-11,271
1956-12,306
1957-01,315
1957-02,301
1957-03,356
1957-04,348
1957-05,355
1957-06,422
1957-07,465
1957-08,467
1957-09,404
1957-10,347
1957-11,305
1957-12,336
1958-01,340
1958-02,318
1958-03,362
1958-04,348
1958-05,363
1958-06,435
1958-07,491
1958-08,505
1958-09,404
1958-10,359
1958-11,310
1958-12,337
1959-01,360
1959-02,342
1959-03,406
1959-04,396
1959-05,420
1959-06,472
1959-07,548
1959-08,559
1959-09,463
1959-10,407
1959-11,362
1959-12,405
1960-01,417
1960-02,391
1960-03,419
1960-04,461
1960-05,472
1960-06,535
1960-07,622
1960-08,606
1960-09,508
1960-10,461
1960-11,390
1960-12,432
1 Month #Passengers
2 1949-01 112
3 1949-02 118
4 1949-03 132
5 1949-04 129
6 1949-05 121
7 1949-06 135
8 1949-07 148
9 1949-08 148
10 1949-09 136
11 1949-10 119
12 1949-11 104
13 1949-12 118
14 1950-01 115
15 1950-02 126
16 1950-03 141
17 1950-04 135
18 1950-05 125
19 1950-06 149
20 1950-07 170
21 1950-08 170
22 1950-09 158
23 1950-10 133
24 1950-11 114
25 1950-12 140
26 1951-01 145
27 1951-02 150
28 1951-03 178
29 1951-04 163
30 1951-05 172
31 1951-06 178
32 1951-07 199
33 1951-08 199
34 1951-09 184
35 1951-10 162
36 1951-11 146
37 1951-12 166
38 1952-01 171
39 1952-02 180
40 1952-03 193
41 1952-04 181
42 1952-05 183
43 1952-06 218
44 1952-07 230
45 1952-08 242
46 1952-09 209
47 1952-10 191
48 1952-11 172
49 1952-12 194
50 1953-01 196
51 1953-02 196
52 1953-03 236
53 1953-04 235
54 1953-05 229
55 1953-06 243
56 1953-07 264
57 1953-08 272
58 1953-09 237
59 1953-10 211
60 1953-11 180
61 1953-12 201
62 1954-01 204
63 1954-02 188
64 1954-03 235
65 1954-04 227
66 1954-05 234
67 1954-06 264
68 1954-07 302
69 1954-08 293
70 1954-09 259
71 1954-10 229
72 1954-11 203
73 1954-12 229
74 1955-01 242
75 1955-02 233
76 1955-03 267
77 1955-04 269
78 1955-05 270
79 1955-06 315
80 1955-07 364
81 1955-08 347
82 1955-09 312
83 1955-10 274
84 1955-11 237
85 1955-12 278
86 1956-01 284
87 1956-02 277
88 1956-03 317
89 1956-04 313
90 1956-05 318
91 1956-06 374
92 1956-07 413
93 1956-08 405
94 1956-09 355
95 1956-10 306
96 1956-11 271
97 1956-12 306
98 1957-01 315
99 1957-02 301
100 1957-03 356
101 1957-04 348
102 1957-05 355
103 1957-06 422
104 1957-07 465
105 1957-08 467
106 1957-09 404
107 1957-10 347
108 1957-11 305
109 1957-12 336
110 1958-01 340
111 1958-02 318
112 1958-03 362
113 1958-04 348
114 1958-05 363
115 1958-06 435
116 1958-07 491
117 1958-08 505
118 1958-09 404
119 1958-10 359
120 1958-11 310
121 1958-12 337
122 1959-01 360
123 1959-02 342
124 1959-03 406
125 1959-04 396
126 1959-05 420
127 1959-06 472
128 1959-07 548
129 1959-08 559
130 1959-09 463
131 1959-10 407
132 1959-11 362
133 1959-12 405
134 1960-01 417
135 1960-02 391
136 1960-03 419
137 1960-04 461
138 1960-05 472
139 1960-06 535
140 1960-07 622
141 1960-08 606
142 1960-09 508
143 1960-10 461
144 1960-11 390
145 1960-12 432

1225
kaggle_time_series_air_passenger/model.ipynb Normal file
View File

File diff suppressed because one or more lines are too long

404
kaggle_time_series_air_passenger/model2.ipynb Normal file
View File

File diff suppressed because one or more lines are too long

BIN
kaggle_time_series_air_passenger/my_checkpoint/saved_model.pb Normal file
View File

Binary file not shown.

BIN
kaggle_time_series_air_passenger/my_checkpoint/variables/variables.data-00000-of-00001 Normal file
View File

Binary file not shown.

BIN
kaggle_time_series_air_passenger/my_checkpoint/variables/variables.index Normal file
View File

Binary file not shown.