diff --git a/2-Regression/3-Linear/notebook.ipynb b/2-Regression/3-Linear/notebook.ipynb
index 2da56e5b..b01f1ee8 100644
--- a/2-Regression/3-Linear/notebook.ipynb
+++ b/2-Regression/3-Linear/notebook.ipynb
@@ -16,209 +16,9 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "
\n",
- "\n",
- "
\n",
- " \n",
- " \n",
- " \n",
- " City Name \n",
- " Type \n",
- " Package \n",
- " Variety \n",
- " Sub Variety \n",
- " Grade \n",
- " Date \n",
- " Low Price \n",
- " High Price \n",
- " Mostly Low \n",
- " ... \n",
- " Unit of Sale \n",
- " Quality \n",
- " Condition \n",
- " Appearance \n",
- " Storage \n",
- " Crop \n",
- " Repack \n",
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- " Unnamed: 24 \n",
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- " 24 inch bins \n",
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- " NaN \n",
- " NaN \n",
- " 4/29/17 \n",
- " 270.0 \n",
- " 280.0 \n",
- " 270.0 \n",
- " ... \n",
- " NaN \n",
- " NaN \n",
- " NaN \n",
- " NaN \n",
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- " BALTIMORE \n",
- " NaN \n",
- " 24 inch bins \n",
- " NaN \n",
- " NaN \n",
- " NaN \n",
- " 5/6/17 \n",
- " 270.0 \n",
- " 280.0 \n",
- " 270.0 \n",
- " ... \n",
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- " NaN \n",
- " NaN \n",
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- " NaN \n",
- " \n",
- " \n",
- " 2 \n",
- " BALTIMORE \n",
- " NaN \n",
- " 24 inch bins \n",
- " HOWDEN TYPE \n",
- " NaN \n",
- " NaN \n",
- " 9/24/16 \n",
- " 160.0 \n",
- " 160.0 \n",
- " 160.0 \n",
- " ... \n",
- " NaN \n",
- " NaN \n",
- " NaN \n",
- " NaN \n",
- " NaN \n",
- " NaN \n",
- " N \n",
- " NaN \n",
- " NaN \n",
- " NaN \n",
- " \n",
- " \n",
- " 3 \n",
- " BALTIMORE \n",
- " NaN \n",
- " 24 inch bins \n",
- " HOWDEN TYPE \n",
- " NaN \n",
- " NaN \n",
- " 9/24/16 \n",
- " 160.0 \n",
- " 160.0 \n",
- " 160.0 \n",
- " ... \n",
- " NaN \n",
- " NaN \n",
- " NaN \n",
- " NaN \n",
- " NaN \n",
- " NaN \n",
- " N \n",
- " NaN \n",
- " NaN \n",
- " NaN \n",
- " \n",
- " \n",
- " 4 \n",
- " BALTIMORE \n",
- " NaN \n",
- " 24 inch bins \n",
- " HOWDEN TYPE \n",
- " NaN \n",
- " NaN \n",
- " 11/5/16 \n",
- " 90.0 \n",
- " 100.0 \n",
- " 90.0 \n",
- " ... \n",
- " NaN \n",
- " NaN \n",
- " NaN \n",
- " NaN \n",
- " NaN \n",
- " NaN \n",
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- " NaN \n",
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- " NaN \n",
- " \n",
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- "
5 rows × 26 columns
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- "
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- ],
- "text/plain": [
- " City Name Type Package Variety Sub Variety Grade Date \\\n",
- "0 BALTIMORE NaN 24 inch bins NaN NaN NaN 4/29/17 \n",
- "1 BALTIMORE NaN 24 inch bins NaN NaN NaN 5/6/17 \n",
- "2 BALTIMORE NaN 24 inch bins HOWDEN TYPE NaN NaN 9/24/16 \n",
- "3 BALTIMORE NaN 24 inch bins HOWDEN TYPE NaN NaN 9/24/16 \n",
- "4 BALTIMORE NaN 24 inch bins HOWDEN TYPE NaN NaN 11/5/16 \n",
- "\n",
- " Low Price High Price Mostly Low ... Unit of Sale Quality Condition \\\n",
- "0 270.0 280.0 270.0 ... NaN NaN NaN \n",
- "1 270.0 280.0 270.0 ... NaN NaN NaN \n",
- "2 160.0 160.0 160.0 ... NaN NaN NaN \n",
- "3 160.0 160.0 160.0 ... NaN NaN NaN \n",
- "4 90.0 100.0 90.0 ... NaN NaN NaN \n",
- "\n",
- " Appearance Storage Crop Repack Trans Mode Unnamed: 24 Unnamed: 25 \n",
- "0 NaN NaN NaN E NaN NaN NaN \n",
- "1 NaN NaN NaN E NaN NaN NaN \n",
- "2 NaN NaN NaN N NaN NaN NaN \n",
- "3 NaN NaN NaN N NaN NaN NaN \n",
- "4 NaN NaN NaN N NaN NaN NaN \n",
- "\n",
- "[5 rows x 26 columns]"
- ]
- },
- "execution_count": 2,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
@@ -232,115 +32,9 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "\n",
- "\n",
- "
\n",
- " \n",
- " \n",
- " \n",
- " Month \n",
- " Variety \n",
- " City \n",
- " Package \n",
- " Low Price \n",
- " High Price \n",
- " Price \n",
- " \n",
- " \n",
- " \n",
- " \n",
- " 70 \n",
- " 9 \n",
- " PIE TYPE \n",
- " BALTIMORE \n",
- " 1 1/9 bushel cartons \n",
- " 15.0 \n",
- " 15.0 \n",
- " 13.636364 \n",
- " \n",
- " \n",
- " 71 \n",
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- " PIE TYPE \n",
- " BALTIMORE \n",
- " 1 1/9 bushel cartons \n",
- " 18.0 \n",
- " 18.0 \n",
- " 16.363636 \n",
- " \n",
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- " 10 \n",
- " PIE TYPE \n",
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- " 1 1/9 bushel cartons \n",
- " 18.0 \n",
- " 18.0 \n",
- " 16.363636 \n",
- " \n",
- " \n",
- " 73 \n",
- " 10 \n",
- " PIE TYPE \n",
- " BALTIMORE \n",
- " 1 1/9 bushel cartons \n",
- " 17.0 \n",
- " 17.0 \n",
- " 15.454545 \n",
- " \n",
- " \n",
- " 74 \n",
- " 10 \n",
- " PIE TYPE \n",
- " BALTIMORE \n",
- " 1 1/9 bushel cartons \n",
- " 15.0 \n",
- " 15.0 \n",
- " 13.636364 \n",
- " \n",
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- "
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- "
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- ],
- "text/plain": [
- " Month Variety City Package Low Price High Price \\\n",
- "70 9 PIE TYPE BALTIMORE 1 1/9 bushel cartons 15.0 15.0 \n",
- "71 9 PIE TYPE BALTIMORE 1 1/9 bushel cartons 18.0 18.0 \n",
- "72 10 PIE TYPE BALTIMORE 1 1/9 bushel cartons 18.0 18.0 \n",
- "73 10 PIE TYPE BALTIMORE 1 1/9 bushel cartons 17.0 17.0 \n",
- "74 10 PIE TYPE BALTIMORE 1 1/9 bushel cartons 15.0 15.0 \n",
- "\n",
- " Price \n",
- "70 13.636364 \n",
- "71 16.363636 \n",
- "72 16.363636 \n",
- "73 15.454545 \n",
- "74 13.636364 "
- ]
- },
- "execution_count": 3,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"pumpkins = pumpkins[pumpkins['Package'].str.contains('bushel', case=True, regex=True)]\n",
"\n",
@@ -377,34 +71,9 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "(array([ 7.5, 8. , 8.5, 9. , 9.5, 10. , 10.5, 11. , 11.5, 12. , 12.5]),\n",
- " )"
- ]
- },
- "execution_count": 4,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
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