Linear Regression Machine Learning Project.Rmd

# Linear Regression Machine Learning Project With R


# Read the Data

```{r}
bike <- read.csv('./bikeshare.csv')
head(bike)
```


## Exploratory Data Analysis


# Load the Libraries

```{r}
library(ggplot2)
library(dplyr)
```

```{r}
ggplot(bike,aes(temp,count)) + geom_point(alpha=0.3,aes(color=temp)) + theme_bw()
```

# Convert to Timestamp
```{r}
bike$datetime <- as.POSIXct(bike$datetime)
```

```{r}
plot <- ggplot(bike,aes(datetime,count)) + geom_point(aes(color=temp), alpha = 0.5)
print(plot)
```


```{r}
plot + scale_color_continuous(low='#55D8CE',high = '#FF6E2E') + theme_bw()
```


# Building a Correlation Graph


```{r}
cor(bike[,c('temp','count')])
ggplot(bike,aes(factor(season),count)) + geom_boxplot(aes(color=factor(season))) + theme_bw()
```


# Feature Engineering

```{r}
bike$hour <- sapply(bike$datetime,function(x){format(x, "%H")})
head(bike)
```

# Scatterplot

## For Working Days

```{r}
plot <- ggplot(filter(bike,workingday==1),aes(hour,count))
```

```{r}
plot <- plot + geom_point(position=position_jitter(w=1, h=0),aes(color=temp),alpha=0.5)
plot <- plot + scale_color_gradientn(colours = c('dark blue','blue','light blue','light green','yellow','orange','red'))
plot + theme_bw()
```


## For Non-Working Days

```{r}
plot <- ggplot(filter(bike,workingday==0),aes(hour,count)) 
plot <- plot + geom_point(position=position_jitter(w=1, h=0),aes(color=temp),alpha=0.8)
```

```{r}
plot <- plot + scale_color_gradientn(colours = c('dark blue','blue','light blue','light green','yellow','orange','red'))
plot + theme_bw()
```

## Building the Linear Model

```{r}
temp.model <- lm(count~temp,bike)
```

## Getting the Summary of the Model

```{r}
summary(temp.model)
```

## How many bike rentals would we predict if the temperature was 25 degrees Celsius? Calculate this two ways:

# * Using the values we just got above
# * Using the predict() function


# Method 1
```{r}
6.0462 + 9.17*25
```


# Method 2

```{r}
temp.test <- data.frame(temp=c(25))
predict(temp.model,temp.test)
```

# Columns of Numeric Values

```{r}
bike$hour <- sapply(bike$hour,as.numeric)
```

# Building Models Based on Different Variables


```{r}
model <- lm(count ~ . -casual - registered -datetime -atemp,bike )
summary(model)
```