diff --git a/module-1/.gitignore b/module-1/.gitignore index b2e5ef3d..1fb4aecd 100644 --- a/module-1/.gitignore +++ b/module-1/.gitignore @@ -1,2 +1,3 @@ DS_Store -.ipynb_checkpoints \ No newline at end of file +.ipynb_checkpoints +.bash_profile diff --git a/module-3/lab-unsupervised-learning/your-code/main.ipynb b/module-3/lab-unsupervised-learning/your-code/main.ipynb index 9d986f93..dcb1599d 100644 --- a/module-3/lab-unsupervised-learning/your-code/main.ipynb +++ b/module-3/lab-unsupervised-learning/your-code/main.ipynb @@ -12,7 +12,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -22,7 +22,8 @@ "\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", - "import pandas as pd" + "import pandas as pd\n", + "import seaborn as sns" ] }, { @@ -38,7 +39,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -67,20 +68,585 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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ChannelRegionFreshMilkGroceryFrozenDetergents_PaperDelicassen
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ChannelRegionFreshMilkGroceryFrozenDetergents_PaperDelicassen
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mean1.3227272.54318212000.2977275796.2659097951.2772733071.9318182881.4931821524.870455
std0.4680520.77427212647.3288657380.3771759503.1628294854.6733334767.8544482820.105937
min1.0000001.0000003.00000055.0000003.00000025.0000003.0000003.000000
25%1.0000002.0000003127.7500001533.0000002153.000000742.250000256.750000408.250000
50%1.0000003.0000008504.0000003627.0000004755.5000001526.000000816.500000965.500000
75%2.0000003.00000016933.7500007190.25000010655.7500003554.2500003922.0000001820.250000
max2.0000003.000000112151.00000073498.00000092780.00000060869.00000040827.00000047943.000000
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" + ], + "text/plain": [ + " Channel Region Fresh Milk Grocery \\\n", + "count 440.000000 440.000000 440.000000 440.000000 440.000000 \n", + "mean 1.322727 2.543182 12000.297727 5796.265909 7951.277273 \n", + "std 0.468052 0.774272 12647.328865 7380.377175 9503.162829 \n", + "min 1.000000 1.000000 3.000000 55.000000 3.000000 \n", + "25% 1.000000 2.000000 3127.750000 1533.000000 2153.000000 \n", + "50% 1.000000 3.000000 8504.000000 3627.000000 4755.500000 \n", + "75% 2.000000 3.000000 16933.750000 7190.250000 10655.750000 \n", + "max 2.000000 3.000000 112151.000000 73498.000000 92780.000000 \n", + "\n", + " Frozen Detergents_Paper Delicassen \n", + "count 440.000000 440.000000 440.000000 \n", + "mean 3071.931818 2881.493182 1524.870455 \n", + "std 4854.673333 4767.854448 2820.105937 \n", + "min 25.000000 3.000000 3.000000 \n", + "25% 742.250000 256.750000 408.250000 \n", + "50% 1526.000000 816.500000 965.500000 \n", + "75% 3554.250000 3922.000000 1820.250000 \n", + "max 60869.000000 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ChannelRegionFreshMilkGroceryFrozenDetergents_PaperDelicassen
count440.000000440.000000440.000000440.000000440.000000440.000000440.000000440.000000
mean1.3227272.54318212000.2977275796.2659097951.2772733071.9318182881.4931821524.870455
std0.4680520.77427212647.3288657380.3771759503.1628294854.6733334767.8544482820.105937
min1.0000001.0000003.00000055.0000003.00000025.0000003.0000003.000000
25%1.0000002.0000003127.7500001533.0000002153.000000742.250000256.750000408.250000
50%1.0000003.0000008504.0000003627.0000004755.5000001526.000000816.500000965.500000
75%2.0000003.00000016933.7500007190.25000010655.7500003554.2500003922.0000001820.250000
max2.0000003.000000112151.00000073498.00000092780.00000060869.00000040827.00000047943.000000
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" + ], + "text/plain": [ + " Channel Region Fresh Milk Grocery \\\n", + "count 440.000000 440.000000 440.000000 440.000000 440.000000 \n", + "mean 1.322727 2.543182 12000.297727 5796.265909 7951.277273 \n", + "std 0.468052 0.774272 12647.328865 7380.377175 9503.162829 \n", + "min 1.000000 1.000000 3.000000 55.000000 3.000000 \n", + "25% 1.000000 2.000000 3127.750000 1533.000000 2153.000000 \n", + "50% 1.000000 3.000000 8504.000000 3627.000000 4755.500000 \n", + "75% 2.000000 3.000000 16933.750000 7190.250000 10655.750000 \n", + "max 2.000000 3.000000 112151.000000 73498.000000 92780.000000 \n", + "\n", + " Frozen Detergents_Paper Delicassen \n", + "count 440.000000 440.000000 440.000000 \n", + "mean 3071.931818 2881.493182 1524.870455 \n", + "std 4854.673333 4767.854448 2820.105937 \n", + "min 25.000000 3.000000 3.000000 \n", + "25% 742.250000 256.750000 408.250000 \n", + "50% 1526.000000 816.500000 965.500000 \n", + "75% 3554.250000 3922.000000 1820.250000 \n", + "max 60869.000000 40827.000000 47943.000000 " + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "customers.describe()\n", + "# Your observations here\n", + "#The dataset contains only numerical data, \n", + "#there aren't nulls values. There are no extreme correlations" ] }, { @@ -94,20 +660,177 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ - "# Your code here" + "# Your code here\n", + "customers.drop(columns=['Channel', 'Region'], inplace=True)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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FreshMilkGroceryFrozenDetergents_PaperDelicassen
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.....................
435297031205116027131351822204
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440 rows × 6 columns

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" + ], + "text/plain": [ + " Fresh Milk Grocery Frozen Detergents_Paper Delicassen\n", + "0 12669 9656 7561 214 2674 1338\n", + "1 7057 9810 9568 1762 3293 1776\n", + "2 6353 8808 7684 2405 3516 7844\n", + "3 13265 1196 4221 6404 507 1788\n", + "4 22615 5410 7198 3915 1777 5185\n", + ".. ... ... ... ... ... ...\n", + "435 29703 12051 16027 13135 182 2204\n", + "436 39228 1431 764 4510 93 2346\n", + "437 14531 15488 30243 437 14841 1867\n", + "438 10290 1981 2232 1038 168 2125\n", + "439 2787 1698 2510 65 477 52\n", + "\n", + "[440 rows x 6 columns]" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# Your comment here" + "customers" ] }, { @@ -127,7 +850,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -135,7 +858,9 @@ "\n", "from sklearn.preprocessing import StandardScaler\n", "\n", - "# Your code here:\n" + "# Your code here:\n", + "StandardScaler = StandardScaler().fit(customers)\n", + "customers_scale = StandardScaler.transform(customers)" ] }, { @@ -149,13 +874,183 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "metadata": { "scrolled": true }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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FreshMilkGroceryFrozenDetergents_PaperDelicassenlabels
01266996567561214267413386
17057981095681762329317766
26353880876842405351678446
31326511964221640450717882
422615541071983915177751850
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" + ], + "text/plain": [ + " Fresh Milk Grocery Frozen Detergents_Paper Delicassen labels\n", + "0 12669 9656 7561 214 2674 1338 6\n", + "1 7057 9810 9568 1762 3293 1776 6\n", + "2 6353 8808 7684 2405 3516 7844 6\n", + "3 13265 1196 4221 6404 507 1788 2\n", + "4 22615 5410 7198 3915 1777 5185 0\n", + "5 9413 8259 5126 666 1795 1451 2\n", + "6 12126 3199 6975 480 3140 545 2\n", + "7 7579 4956 9426 1669 3321 2566 6\n", + "8 5963 3648 6192 425 1716 750 2\n", + "9 6006 11093 18881 1159 7425 2098 6\n", + "10 3366 5403 12974 4400 5977 1744 6" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# Your code here:\n" + "# Your code here:\n", + "from sklearn.cluster import KMeans\n", + "kmeans = KMeans()\n", + "customers_kmeans = kmeans.fit(customers)\n", + "customers['labels'] = customers_kmeans.labels_\n", + "customers.head(11)" ] }, { @@ -167,11 +1062,31 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "2 186\n", + "0 101\n", + "6 93\n", + "1 31\n", + "3 22\n", + "4 4\n", + "5 2\n", + "7 1\n", + "Name: labels, dtype: int64" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# Your code here:\n" + "# Your code here:\n", + "customers.labels.value_counts()" ] }, { @@ -185,13 +1100,130 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "metadata": { "scrolled": true }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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FreshMilkGroceryFrozenDetergents_PaperDelicassenlabelslabels_DBSCAN
012669965675612142674133860
170579810956817623293177660
26353880876842405351678446-1
313265119642216404507178820
422615541071983915177751850-1
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" + ], + "text/plain": [ + " Fresh Milk Grocery Frozen Detergents_Paper Delicassen labels \\\n", + "0 12669 9656 7561 214 2674 1338 6 \n", + "1 7057 9810 9568 1762 3293 1776 6 \n", + "2 6353 8808 7684 2405 3516 7844 6 \n", + "3 13265 1196 4221 6404 507 1788 2 \n", + "4 22615 5410 7198 3915 1777 5185 0 \n", + "\n", + " labels_DBSCAN \n", + "0 0 \n", + "1 0 \n", + "2 -1 \n", + "3 0 \n", + "4 -1 " + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# Your code here\n" + "# Your code here\n", + "from sklearn.cluster import DBSCAN\n", + "dbscan = DBSCAN(eps = 0.5)\n", + "customers_dbscan = dbscan.fit(customers_scale)\n", + "customers['labels_DBSCAN'] = customers_dbscan.labels_\n", + "customers.head()" ] }, { @@ -203,11 +1235,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + " 0 261\n", + "-1 174\n", + " 1 5\n", + "Name: labels_DBSCAN, dtype: int64" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# Your code here\n" + "# Your code here\n", + "customers.labels_DBSCAN.value_counts()" ] }, { @@ -364,9 +1411,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "ironhack_env", "language": "python", - "name": "python3" + "name": "ironhack_env" }, "language_info": { "codemirror_mode": { @@ -378,7 +1425,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.0" + "version": "3.7.5" } }, "nbformat": 4,